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Presentation held on the Opendata.ch-Conference 2012, Zurich
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Main Library, Open Access
Open Access: Achievements & Challenges Christian Gutknecht, Main Library University of Zurich
Opendata.ch 2012 Conference 28.6.2012, Zurich
www.oai.uzh.ch
(except University Logo)
Main Library, Open Access
http://www.guardian.co.uk/science/2012/jun/08/open-access-research-inevitable-nature-editor
2
Main Library, Open Access
http://www.guardian.co.uk/science/2012/jun/19/open-access-academic-publishing-finch-report
3
Main Library, Open Access
http://www.guardian.co.uk/science/2012/apr/24/harvard-university-journal-publishers-prices
4
Main Library, Open Access
Open Access via Repository (Green Road)
Publisher researchers
publish a paper in a traditional journal
Publisher
from other researchers in the field were denied [16]. These results
do not include other data practices which may also negatively
affect the progress of science, such as significant delays in the
fulfillment of requests, refusals to publicly present research
findings, and the failure to discuss research with others [16].
Disciplines or subdisciplines have their own culture of data-
sharing. Some do better (geophysics, biodiversity, and astronomy)
than others [17].
Individual Choice vs. Institutional Policies
The extent to which researchers share or withhold data is not
primarily an individual choice. Underlying policies and practices
have great influence on encouraging or inhibiting data sharing.
Several researchers who failed to share their data in the study by
Savage and Vickers, et al., claimed that it would take too much
work to provide raw data. The authors came to the conclusion that
researchers often fail to develop clear, well-annotated datasets to
accompany their research (i.e., metadata), and may lose access and
understanding of the original dataset over time. Vickers, et al.
believe that a policy that would require authors to submit datasets
to journals or public repositories at the time of publication would
help to prevent this occurrence [11]. PARSE Insight, a project
concerned with the preservation of digital information in research,
reported from a survey of data managers that 64% claimed their
organizations had policies and procedures in place to determine
what kinds of data are accepted for storage and preservation, with
specific policies for the time frame and method of submission.
Though this number constitutes a majority, 32% reported a lack of
such policies or procedures [8].Policies and procedures sometimes serve as an active rather
than passive barrier to data sharing. Campbell et al. (2003)
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
[5] [19].
Table 2. Subject discipline.
Frequency Percent
environmental sciences & ecology475
36.1
social sciences
20415.5
biology
18113.7
physical sciences
15812.0
computer science/engineering118
9.0
other
987.4
atmospheric science
523.9
medicine
312.4
Total
1317100.0
doi:10.1371/journal.pone.0021101.t002
Table 3. Data access.
Frequency Percent
An organization-specific system
35138.5%
Long-tem Ecological Research Network292
32.1%
Other data access
24627.0%
A Distributed Active-Archive Center
17319.0%
A Global Biodiversity Information Facility73
8.0%
National Biological Information Infrastructure70
7.7%
National Ecological Observatory Network64
7.0%
International Long-term Ecological Research Network 586.4%
Taiwan Ecological Research Network
7.8%
South African Environmental Observation Network 6.7%
doi:10.1371/journal.pone.0021101.t003
Table 4. Data types.
ResponsesPercent
Experimental
71154.6%
Observational
63248.5%
Data Models
49938.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
Table 1. Primary work sector.
FrequencyPercent
Academic1058
80.5
Government167
12.7
Commercial34
2.6
Non-profit35
2.7
Other
21
1.6
Total
1315
100.0doi:10.1371/journal.pone.0021101.t001
Data Sharing by Scientists
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
from other researchers in the field were denied [16]. These results
do not include other data practices which may also negatively
affect the progress of science, such as significant delays in the
fulfillment of requests, refusals to publicly present research
findings, and the failure to discuss research with others [16].
Disciplines or subdisciplines have their own culture of data-
sharing. Some do better (geophysics, biodiversity, and astronomy)The extent to which researchers share or withhold data is not
primarily an individual choice. Underlying policies and practices
have great influence on encouraging or inhibiting data sharing.
Several researchers who failed to share their data in the study by
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
[5] [19].
Table 4. Data types.
Experimental
71154.6%
Experimental
71154.6%
Observational
63248.5%
Observational
63248.5%
Data Models
49938.3%
Data Models
49938.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Interviews
19515.0%
Other
80
6.1%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
most important reason for data preservation. Nearly all (98%) ofparticipants agreed that if research is publicly funded, the resultsshould become public property and therefore properly preserved[8].This article reports the results of a survey of scientists’ current
data sharing practices and their perceptions of the barriers andenablers of data sharing. The survey was conducted by theresearch team of the National Science Foundation-fundedDataONE project. DataNet supports short- and long-term datamanagement and open access to data. DataONE is one of theinitially funded NSF DataNet partners. DataONE is a large scalecollaboration to develop an organization that supports the fullinformation lifecycle of biological, ecological, and environmentaldata and tools to be used by researchers, educators, students,decision-makers and the general public. DataONE ‘‘will ensurethe preservation and access to multi-scale, multi-discipline, andmulti-national science data’’ [9] by developing a strong cyberin-frastructure and community engagement programs.DataONE will (i) provide coordinated access to current data
collections; (ii) create a new global cyberinfrastructure thatcontains both biological and environmental data coming fromdifferent resources (research networks, environmental observato-ries, individual scientists, and citizen scientists); and (iii) change thescience culture and institutions by providing education andtraining, engaging citizens in science, and building globalcommunities of practice. In order to facilitate change of thescience culture through cyberinfrastructure for data, it is necessaryto first understand the culture of modern science and the role ofdata in it.
Data SharingEncouraging data sharing and reuse begins with good data
practices in all phases of the data lifecycle such as generating andcollecting the data, managing the data, analyzing the data, andsharing it. However, the data lifecycle cannot be consideredindependently from research lifecycle [10], as data are anindispensible element of scientific research. (See Figure 1.)The specific costs of handling supplementary materials such as
datasets are not well documented. In a recent survey, only authorfees and journal subscription fees were mentioned as currentfunding sources for supplementary materials in journals. Partic-ipants in the survey suggested other potential sources for funding,in particular government funding, support from learned societies,and publishers [11].
Data Sharing/Withholding PracticesData sharing is important. According to a study done by
Publishing Research Consortium (PRC) in 2010 with 3823respondents, access to datasets, data models, and algorithms &programs was ranked important or highly important; however,only 38% of them felt that they were easily accessible [12]. Inaddition, it was the lowest among the other information types(some of them were research articles in journals, reference works,technical information, patent information, etc.). Several previoussurveys have explored the benefits and barriers of sharing data[13] and the extent to which researchers share or withhold data.Results seem to suggest that current sharing practices are minimal,although the amount of data sharing varies among different fields.Some journals have specific guidelines which require authors toshare their data with other researchers. However, the extent towhich these guidelines are carried out remains largely untested.Savage and Vickers requested data from ten researchers who hadpublished articles in PLoS journals, which have specific datasharing policies. Only one author sent an original dataset [14].
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subjectdiscipline.Researchers who choose to withhold datasets often have specific
reasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
PublisherPublisher
most important reason for data preservation. Nearly all (98%) ofparticipants agreed that if research is publicly funded, the resultsshould become public property and therefore properly preserved[8].This article reports the results of a survey of scientists’ current
data sharing practices and their perceptions of the barriers andenablers of data sharing. The survey was conducted by theresearch team of the National Science Foundation-fundedDataONE project. DataNet supports short- and long-term datamanagement and open access to data. DataONE is one of theinitially funded NSF DataNet partners. DataONE is a large scalecollaboration to develop an organization that supports the fullinformation lifecycle of biological, ecological, and environmentaldata and tools to be used by researchers, educators, students,decision-makers and the general public. DataONE ‘‘will ensurethe preservation and access to multi-scale, multi-discipline, andmulti-national science data’’ [9] by developing a strong cyberin-frastructure and community engagement programs.DataONE will (i) provide coordinated access to current data
collections; (ii) create a new global cyberinfrastructure thatcontains both biological and environmental data coming fromdifferent resources (research networks, environmental observato-ries, individual scientists, and citizen scientists); and (iii) change thescience culture and institutions by providing education andtraining, engaging citizens in science, and building globalcommunities of practice. In order to facilitate change of thescience culture through cyberinfrastructure for data, it is necessaryto first understand the culture of modern science and the role ofdata in it.
Data SharingEncouraging data sharing and reuse begins with good data
practices in all phases of the data lifecycle such as generating andcollecting the data, managing the data, analyzing the data, andsharing it. However, the data lifecycle cannot be consideredindependently from research lifecycle [10], as data are anindispensible element of scientific research. (See Figure 1.)The specific costs of handling supplementary materials such as
datasets are not well documented. In a recent survey, only authorfees and journal subscription fees were mentioned as currentfunding sources for supplementary materials in journals. Partic-ipants in the survey suggested other potential sources for funding,in particular government funding, support from learned societies,and publishers [11].
Data Sharing/Withholding PracticesData sharing is important. According to a study done by
Publishing Research Consortium (PRC) in 2010 with 3823respondents, access to datasets, data models, and algorithms &programs was ranked important or highly important; however,only 38% of them felt that they were easily accessible [12]. Inaddition, it was the lowest among the other information types(some of them were research articles in journals, reference works,technical information, patent information, etc.). Several previoussurveys have explored the benefits and barriers of sharing data[13] and the extent to which researchers share or withhold data.Results seem to suggest that current sharing practices are minimal,although the amount of data sharing varies among different fields.Some journals have specific guidelines which require authors toshare their data with other researchers. However, the extent towhich these guidelines are carried out remains largely untested.Savage and Vickers requested data from ten researchers who hadpublished articles in PLoS journals, which have specific datasharing policies. Only one author sent an original dataset [14].
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subjectdiscipline.Researchers who choose to withhold datasets often have specific
reasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
Data Sharingby Scientis
ts: Practices and
Perceptions
Carol Tenopir
1*, SuzieAllard
1, Kimberly Douglass
1, ArsevUmur Ayd
inoglu1, Lei W
u1, Eleano
r Read2,
MaribethManoff
2, Mike Frame3
1 School of Inform
ation Sciences, Univers
ity of Tennessee, Kn
oxville, Tennesse
e, United States o
f America, 2Universi
ty of Tennessee Libraries
, University of Tenn
essee,
Knoxville, Tennes
see, United States o
f America, 3Center f
or Biological Inf
ormatics, United States G
eological Survey
, Oak Ridge, Tennesse
e, United States o
f America
Abstract
Background: Scie
ntific research in the 21st cen
tury is more data inte
nsive and collaborative than in the past. It i
s important
to study the data practice
s of researchers
– data accessibility, disc
overy, re-use, pre
servation and, par
ticularly, data sharing.
Data sharing is a valu
able part of the
scientificmethod allowing
for verification of result
s and extending research
from prior
results.
Methodology/Princ
ipal Findings: A
total of1329 scientist
s participated in this survey explorin
g currentdata sharing
practices and percepti
ons of the barriersand enablers
of datasharing.
Scientists do not make their da
ta electronically
availableto others fo
r variousreasons,
including insufficie
nt time and lack of funding. Most resp
ondentsare satisfied
with
their current pro
cesses for the initial an
d short-term parts of
the data or research lifecycle
(collecting their res
earch data;
searching for, desc
ribing or cataloging, an
alyzing,and short-te
rm storageof their
data) but are not satis
fied with long-term
data preservatio
n. Many organization
s do not provide support
to their researchers
for datamanagem
ent bothin the short-
and long-term. If certa
in conditions are m
et (suchas formal citatio
n and sharingreprints)
respondents agr
ee theyare willin
g
to share their data. There
are also significant differ
ences and approac
hes in data management prac
tices based on primary
fundingagency,
subjectdisciplin
e, age, work focus, an
d world region.
Conclusions/Sign
ificance:Barriers
to effectivedata sha
ring and preservation are deeply r
ooted in the practices and culture
of the researchprocess
as well as the research
ers themselves. N
ew mandatesfor data
management plan
s from NSF and
other federal ag
encies and world-w
ide attention to the need to share and preserve
data could lead to changes
. Large scale
programs, such as the NSF-spo
nsored DataNET(includin
g projectslike DataON
E) will both bring attentio
n and resources to
the issue and make it easierfor scien
tists to apply sound data management prin
ciples.
Citation: Tenop
ir C, Allard S, Doug
lass K, Aydinoglu AU, Wu L, et al.
(2011) Data Sharing
by Scientists: Practi
ces and Perceptions. PLo
S ONE 6(6): e21101.
doi:10.1371/jour
nal.pone.0021101
Editor:Cameron Neylon,
Scienceand Technol
ogy FacilitiesCouncil,
United Kingdom
Received January
3, 2011;Accept
ed May 20, 2011; Publis
hed June 29, 2011
Copyright: ! 2011 Tenopir
et al. This is an open-ac
cess article distributed under th
e terms of the CreativeCommons Attribut
ion License,which permits
unrestricted use, dist
ribution, and reprodu
ction in any medium, provided the original
author and source are credited
.
Funding: The p
roject was funde
d as part of the Na
tional Science Fo
undation, Division
of Cyberinfrastru
cture, Data Obse
rvation Networkfor Earth
(DataONE) NSF
award #0830944under a
Cooperative Agre
ement. Thefunders
had no role in study design, dat
a collection and analysis,
decisionto publish,
or preparation of the
manuscript.
CompetingInteres
ts: Theauthors
have declaredthat no
competing interestsexist.
* E-mail: ctenopir@ut
k.edu
Introduction
Data are the inf
rastructure of sci
ence. Sound data are
critical as
they form the basi
s for good scientific
decisions, wise m
anagement
and use of resources, an
d informed decision-making. M
oreover,
‘‘scienceis becom
ing data intensive and collabor
ative’’ [1]. The
amount ofdata collected
, analyzed, re-a
nalyzed, and stored has
increased enormously due to develop
ments in computational
simulationand modeling,
automated data acquisition, and
communication technolo
gies [2].Followin
g the previousresearch
paradigms (experim
ental, theoretical, and computation
al), this
new era has been called ‘‘the fourth paradigm: data-int
ensive
scientificdiscover
y’’ where ‘‘all of t
he scienceliteratur
e is online,
all of the science
data is online, and they interope
rate with each
other’’ [3]. Digi
tal dataare not only
the outputsof resea
rch but
provideinputs t
o new hypotheses, ena
bling new scientificinsights
and drivinginnovati
on [4].
As science becom
es more dataintensiv
e and collaborative, da
ta
sharingbecomes more important.
Data sharingincludes
the
deposition and preserva
tion of data; however, it is primarily
associated with providin
g access for use and reuse of data.
Data
sharinghas many advanta
ges, including:
N re-analysis of da
ta helpsverify re
sults data, which
is a keypart
of the scientificprocess;
N different interpre
tationsor approac
hes to existingdata
contribute to scientific
progress–especia
lly in an interdisci-
plinarysetting;
N well-managed,long-ter
m preservation helps retain
data
integrity;
N when data is available, (re-)c
ollectionof data
is minimized;
thus, use of resou
rces is optimized;
N data availability provide
s safeguards against
misconduct
relatedto data fabricati
on and falsification;
N replication studies s
erve as training
tools fornew generati
ons of
researchers [5][6
][7]
Additionally, res
earchers, data managers
and publishers in the
PARSEsurvey o
verwhelmingly ag
reed that public fundi
ng was the
PLoS ONE | www.plosone.o
rg
1
June 2011 | Volume 6 | Issue
6 | e21101
5
Main Library, Open Access
Open Access via Repository (Green Road)
+ deposit a copy on ZORA
zora.uzh.ch
Publisher researchers
publish a paper in a traditional journal
from other researchers in the field were denied [16]. These results
do not include other data practices which may also negatively
affect the progress of science, such as significant delays in the
fulfillment of requests, refusals to publicly present research
findings, and the failure to discuss research with others [16].
Disciplines or subdisciplines have their own culture of data-
sharing. Some do better (geophysics, biodiversity, and astronomy)
than others [17].
Individual Choice vs. Institutional Policies
The extent to which researchers share or withhold data is not
primarily an individual choice. Underlying policies and practices
have great influence on encouraging or inhibiting data sharing.
Several researchers who failed to share their data in the study by
Savage and Vickers, et al., claimed that it would take too much
work to provide raw data. The authors came to the conclusion that
researchers often fail to develop clear, well-annotated datasets to
accompany their research (i.e., metadata), and may lose access and
understanding of the original dataset over time. Vickers, et al.
believe that a policy that would require authors to submit datasets
to journals or public repositories at the time of publication would
help to prevent this occurrence [11]. PARSE Insight, a project
concerned with the preservation of digital information in research,
reported from a survey of data managers that 64% claimed their
organizations had policies and procedures in place to determine
what kinds of data are accepted for storage and preservation, with
specific policies for the time frame and method of submission.
Though this number constitutes a majority, 32% reported a lack of
such policies or procedures [8].Policies and procedures sometimes serve as an active rather
than passive barrier to data sharing. Campbell et al. (2003)
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
[5] [19].
Table 2. Subject discipline.
Frequency Percent
environmental sciences & ecology475
36.1
social sciences
20415.5
biology
18113.7
physical sciences
15812.0
computer science/engineering118
9.0
other
987.4
atmospheric science
523.9
medicine
312.4
Total
1317100.0
doi:10.1371/journal.pone.0021101.t002
Table 3. Data access.
Frequency Percent
An organization-specific system
35138.5%
Long-tem Ecological Research Network292
32.1%
Other data access
24627.0%
A Distributed Active-Archive Center
17319.0%
A Global Biodiversity Information Facility73
8.0%
National Biological Information Infrastructure70
7.7%
National Ecological Observatory Network64
7.0%
International Long-term Ecological Research Network 586.4%
Taiwan Ecological Research Network
7.8%
South African Environmental Observation Network 6.7%
doi:10.1371/journal.pone.0021101.t003
Table 4. Data types.
ResponsesPercent
Experimental
71154.6%
Observational
63248.5%
Data Models
49938.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
Table 1. Primary work sector.
FrequencyPercent
Academic1058
80.5
Government167
12.7
Commercial34
2.6
Non-profit35
2.7
Other
21
1.6
Total
1315
100.0doi:10.1371/journal.pone.0021101.t001
Data Sharing by Scientists
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
most important reason for data preservation. Nearly all (98%) ofparticipants agreed that if research is publicly funded, the resultsshould become public property and therefore properly preserved[8].This article reports the results of a survey of scientists’ current
data sharing practices and their perceptions of the barriers andenablers of data sharing. The survey was conducted by theresearch team of the National Science Foundation-fundedDataONE project. DataNet supports short- and long-term datamanagement and open access to data. DataONE is one of theinitially funded NSF DataNet partners. DataONE is a large scalecollaboration to develop an organization that supports the fullinformation lifecycle of biological, ecological, and environmentaldata and tools to be used by researchers, educators, students,decision-makers and the general public. DataONE ‘‘will ensurethe preservation and access to multi-scale, multi-discipline, andmulti-national science data’’ [9] by developing a strong cyberin-frastructure and community engagement programs.DataONE will (i) provide coordinated access to current data
collections; (ii) create a new global cyberinfrastructure thatcontains both biological and environmental data coming fromdifferent resources (research networks, environmental observato-ries, individual scientists, and citizen scientists); and (iii) change thescience culture and institutions by providing education andtraining, engaging citizens in science, and building globalcommunities of practice. In order to facilitate change of thescience culture through cyberinfrastructure for data, it is necessaryto first understand the culture of modern science and the role ofdata in it.
Data SharingEncouraging data sharing and reuse begins with good data
practices in all phases of the data lifecycle such as generating andcollecting the data, managing the data, analyzing the data, andsharing it. However, the data lifecycle cannot be consideredindependently from research lifecycle [10], as data are anindispensible element of scientific research. (See Figure 1.)The specific costs of handling supplementary materials such as
datasets are not well documented. In a recent survey, only authorfees and journal subscription fees were mentioned as currentfunding sources for supplementary materials in journals. Partic-ipants in the survey suggested other potential sources for funding,in particular government funding, support from learned societies,and publishers [11].
Data Sharing/Withholding PracticesData sharing is important. According to a study done by
Publishing Research Consortium (PRC) in 2010 with 3823respondents, access to datasets, data models, and algorithms &programs was ranked important or highly important; however,only 38% of them felt that they were easily accessible [12]. Inaddition, it was the lowest among the other information types(some of them were research articles in journals, reference works,technical information, patent information, etc.). Several previoussurveys have explored the benefits and barriers of sharing data[13] and the extent to which researchers share or withhold data.Results seem to suggest that current sharing practices are minimal,although the amount of data sharing varies among different fields.Some journals have specific guidelines which require authors toshare their data with other researchers. However, the extent towhich these guidelines are carried out remains largely untested.Savage and Vickers requested data from ten researchers who hadpublished articles in PLoS journals, which have specific datasharing policies. Only one author sent an original dataset [14].
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subjectdiscipline.Researchers who choose to withhold datasets often have specific
reasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
Data Sharingby Scientis
ts: Practices and
Perceptions
, Kimberly Douglass
1, ArsevUmur Ayd
inoglu1, Lei W
u1, Eleano
r Read2,
MaribethManoff
1 School of Inform
ation Sciences, Univers
ity of Tennessee, Kn
oxville, Tennesse
e, United States o
f America, 2Universi
ty of Tennessee Libraries
, University of Tenn
essee,
Knoxville, Tennes
see, United States o
f America, 3Center f
or Biological Inf
ormatics, United States G
eological Survey
, Oak Ridge, Tennesse
e, United States o
f America
Abstract
Background: Scie
ntific research in the 21st cen
tury is more data inte
nsive and collaborative than in the past. It i
s important
to study the data practice
s of researchers
– data accessibility, disc
overy, re-use, pre
servation and, par
ticularly, data sharing.
Data sharing is a valu
able part of the
scientificmethod allowing
for verification of result
s and extending research
from prior
results.
Methodology/Princ
ipal Findings: A
total of1329 scientist
s participated in this survey explorin
g currentdata sharing
practices and percepti
ons of the barriersand enablers
of datasharing.
Scientists do not make their da
ta electronically
availableto others fo
r variousreasons,
including insufficie
nt time and lack of funding. Most resp
ondentsare satisfied
with
their current pro
cesses for the initial an
d short-term parts of
the data or research lifecycle
(collecting their res
earch data;
searching for, desc
ribing or cataloging, an
alyzing,and short-te
rm storageof their
data) but are not satis
fied with long-term
data preservatio
n. Many organization
s do not provide support
to their researchers
for datamanagem
ent bothin the short-
and long-term. If certa
in conditions are m
et (suchas formal citatio
n and sharingreprints)
respondents agr
ee theyare willin
g
to share their data. There
are also significant differ
ences and approac
hes in data management prac
tices based on primary
fundingagency,
subjectdisciplin
e, age, work focus, an
d world region.
Conclusions/Sign
ificance:Barriers
to effectivedata sha
ring and preservation are deeply r
ooted in the practices and culture
of the researchprocess
as well as the research
ers themselves. N
ew mandatesfor data
management plan
s from NSF and
other federal ag
encies and world-w
ide attention to the need to share and preserve
data could lead to changes
. Large scale
programs, such as the NSF-spo
nsored DataNET(includin
g projectslike DataON
E) will both bring attentio
n and resources to
the issue and make it easierfor scien
tists to apply sound data management prin
ciples.
Citation: Tenop
ir C, Allard S, Doug
lass K, Aydinoglu AU, Wu L, et al.
(2011) Data Sharing
by Scientists: Practi
ces and Perceptions. PLo
S ONE 6(6): e21101.
doi:10.1371/jour
nal.pone.0021101
Editor:Cameron Neylon,
Scienceand Technol
ogy FacilitiesCouncil,
United Kingdom
Received January
3, 2011;Accept
ed May 20, 2011; Publis
hed June 29, 2011
Copyright: ! 2011 Tenopir
et al. This is an open-ac
cess article distributed under th
e terms of the CreativeCommons Attribut
ion License,which permits
unrestricted use, dist
ribution, and reprodu
ction in any medium, provided the original
author and source are credited
.
Funding: The p
roject was funde
d as part of the Na
tional Science Fo
undation, Division
of Cyberinfrastru
cture, Data Obse
rvation Networkfor Earth
(DataONE) NSF
award #0830944under a
Cooperative Agre
ement. Thefunders
had no role in study design, dat
a collection and analysis,
decisionto publish,
or preparation of the
manuscript.
CompetingInteres
ts: Theauthors
have declaredthat no
competing interestsexist.
* E-mail: ctenopir@ut
k.edu
Introduction
Data are the inf
rastructure of sci
ence. Sound data are
critical as
they form the basi
s for good scientific
decisions, wise m
anagement
and use of resources, an
d informed decision-making. M
oreover,
‘‘scienceis becom
ing data intensive and collabor
ative’’ [1]. The
amount ofdata collected
, analyzed, re-a
nalyzed, and stored has
increased enormously due to develop
ments in computational
simulationand modeling,
automated data acquisition, and
communication technolo
gies [2].Followin
g the previousresearch
paradigms (experim
ental, theoretical, and computation
al), this
new era has been called ‘‘the fourth paradigm: data-int
ensive
scientificdiscover
y’’ where ‘‘all of t
he scienceliteratur
e is online,
all of the science
data is online, and they interope
rate with each
other’’ [3]. Digi
tal dataare not only
the outputsof resea
rch but
provideinputs t
o new hypotheses, ena
bling new scientificinsights
and drivinginnovati
on [4].
As science becom
es more dataintensiv
e and collaborative, da
ta
sharingbecomes more important.
Data sharingincludes
the
deposition and preserva
tion of data; however, it is primarily
associated with providin
g access for use and reuse of data.
Data
sharinghas many advanta
ges, including:
N re-analysis of da
ta helpsverify re
sults data, which
is a keypart
of the scientificprocess;
N different interpre
tationsor approac
hes to existingdata
contribute to scientific
progress–especia
lly in an interdisci-
plinarysetting;
N well-managed,long-ter
m preservation helps retain
data
integrity;
N when data is available, (re-)c
ollectionof data
is minimized;
thus, use of resou
rces is optimized;
N data availability provide
s safeguards against
misconduct
relatedto data fabricati
on and falsification;
N replication studies s
erve as training
tools fornew generati
ons of
researchers [5][6
][7]
Additionally, res
earchers, data managers
and publishers in the
PARSEsurvey o
verwhelmingly ag
reed that public fundi
ng was the
PLoS ONE | www.plosone.o
rg
1
June 2011 | Volume 6 | Issue
6 | e21101
Open Access via Repository (Green Road)
+ deposit a copy on ZORA
zora.uzh.ch
Publisherresearchers
publish a paper
in a traditional journal
Publisher
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
Table 3. Data access.
Frequency Percent
Frequency Percent
Frequency Percent
Frequency Percent
An organization-specific system
35138.5%
An organization-specific system
35138.5%
An organization-specific system
35138.5%
An organization-specific system
35138.5%
An organization-specific system
35138.5%
An organization-specific system
35138.5%
Long-tem Ecological Research Network292
32.1%
Long-tem Ecological Research Network292
32.1%
Long-tem Ecological Research Network292
32.1%
Long-tem Ecological Research Network292
32.1%
Long-tem Ecological Research Network292
32.1%
Long-tem Ecological Research Network292
32.1%
Other data access
24627.0%
Other data access
24627.0%
Other data access
24627.0%
Other data access
24627.0%
Other data access
24627.0%
Other data access
24627.0%
A Distributed Active-Archive Center
17319.0%
A Distributed Active-Archive Center
17319.0%
A Distributed Active-Archive Center
17319.0%
A Distributed Active-Archive Center
17319.0%
A Distributed Active-Archive Center
17319.0%
A Distributed Active-Archive Center
17319.0%
A Global Biodiversity Information Facility73
8.0%
A Global Biodiversity Information Facility73
8.0%
A Global Biodiversity Information Facility73
8.0%
A Global Biodiversity Information Facility73
8.0%
A Global Biodiversity Information Facility73
8.0%
A Global Biodiversity Information Facility73
8.0%
National Biological Information Infrastructure70
7.7%
National Biological Information Infrastructure70
7.7%
National Biological Information Infrastructure70
7.7%
National Biological Information Infrastructure70
7.7%
National Biological Information Infrastructure70
7.7%
National Biological Information Infrastructure70
7.7%
National Ecological Observatory Network64
7.0%
National Ecological Observatory Network64
7.0%
National Ecological Observatory Network64
7.0%
National Ecological Observatory Network64
7.0%
National Ecological Observatory Network64
7.0%
National Ecological Observatory Network64
7.0%
International Long-term Ecological Research Network 586.4%
International Long-term Ecological Research Network 586.4%
International Long-term Ecological Research Network 586.4%
International Long-term Ecological Research Network 586.4%
International Long-term Ecological Research Network 586.4%
International Long-term Ecological Research Network 586.4%
Taiwan Ecological Research Network
7.8%
Taiwan Ecological Research Network
7.8%
Taiwan Ecological Research Network
7.8%
Taiwan Ecological Research Network
7.8%
Taiwan Ecological Research Network
7.8%
Taiwan Ecological Research Network
7.8%
Taiwan Ecological Research Network
7.8%
South African Environmental Observation Network 6.7%
South African Environmental Observation Network 6.7%
South African Environmental Observation Network 6.7%
South African Environmental Observation Network 6.7%
South African Environmental Observation Network 6.7%
South African Environmental Observation Network 6.7%
doi:10.1371/journal.pone.0021101.t003
Data types.
ResponsesPercent
ResponsesPercent
ResponsesPercent
ResponsesPercent
Experimental
71154.6%
Experimental
71154.6%
Experimental
71154.6%
Experimental
71154.6%
Experimental
71154.6%
Experimental
71154.6%
Observational
63248.5%
Observational
63248.5%
Observational
63248.5%
Observational
63248.5%
Observational
63248.5%
Observational
63248.5%
Data Models
49938.3%
Data Models
49938.3%
Data Models
49938.3%
Data Models
49938.3%
Data Models
49938.3%
Data Models
49938.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Interviews
19515.0%
Interviews
19515.0%
Interviews
19515.0%
Interviews
19515.0%
Interviews
19515.0%
Other
80
6.1%
Other
80
6.1%
Other
80
6.1%
Other
80
6.1%
Other
80
6.1%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
Data Sharing by Scientists
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
from other researchers in the field were denied [16]. These results
do not include other data practices which may also negatively
affect the progress of science, such as significant delays in the
fulfillment of requests, refusals to publicly present research
findings, and the failure to discuss research with others [16].
Disciplines or subdisciplines have their own culture of data-
sharing. Some do better (geophysics, biodiversity, and astronomy)The extent to which researchers share or withhold data is not
primarily an individual choice. Underlying policies and practices
have great influence on encouraging or inhibiting data sharing.
Several researchers who failed to share their data in the study by
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
[5] [19].
Table 4. Data types.
Experimental
71154.6%
Experimental
71154.6%
Observational
63248.5%
Observational
63248.5%
Data Models
49938.3%
Data Models
49938.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Interviews
19515.0%
Other
80
6.1%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subject
Researchers who choose to withhold datasets often have specificreasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stages
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
PublisherPublisher
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subject
Researchers who choose to withhold datasets often have specificreasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subject
Researchers who choose to withhold datasets often have specificreasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
Data Sharingby Scientis
ts: Practices and
Perceptions
, Kimberly Douglass
1, ArsevUmur Ayd
inoglu1, Lei W
u1, Eleano
r Read2,
MaribethManoff
1 School of Inform
ation Sciences, Univers
ity of Tennessee, Kn
oxville, Tennesse
e, United States o
f America, 2Universi
ty of Tennessee Libraries
, University of Tenn
essee,
Knoxville, Tennes
see, United States o
f America, 3Center f
or Biological Inf
ormatics, United States G
eological Survey
, Oak Ridge, Tennesse
e, United States o
f America
Abstract
Abstract
Background:
Background: Scie
ntific research in the 21st cen
tury is more data inte
nsive and collaborative than in the past. It i
s important
Scientific researc
h in the 21st century is m
ore data intensive and collabor
ative than in the past. It is important
to study the data practice
s of researchers
– data accessibility, disc
overy, re-use, pre
servation and, par
ticularly, data sharing.
to study the data practice
s of researchers
– data accessibility, disc
overy, re-use, pre
servation and, par
ticularly, data sharing.
Data sharing is a valu
able part of the
scientificmethod allowing
for verification of result
s and extending research
from prior
Data sharing is a valu
able part of the
scientificmethod allowing
for verification of result
s and extending research
from prior
results.results.
Methodology/Princ
ipal Findings:
Methodology/Princ
ipal Findings: A
total of1329 scientist
s participated in this survey explorin
g currentdata sharing
A total of1329 scientist
s participated in this survey explorin
g currentdata sharing
practices and percepti
ons of the barriersand enablers
of datasharing.
Scientists do not make their da
ta electronically
practices and percepti
ons of the barriersand enablers
of datasharing.
Scientists do not make their da
ta electronically
availableto others fo
r variousreasons,
including insufficie
nt time and lack of funding. Most resp
ondentsare satisfied
with
availableto others fo
r variousreasons,
including insufficie
nt time and lack of funding. Most resp
ondentsare satisfied
with
their current pro
cesses for the initial an
d short-term parts of
the data or research lifecycle
(collecting their res
earch data;
their current pro
cesses for the initial an
d short-term parts of
the data or research lifecycle
(collecting their res
earch data;
searching for, desc
ribing or cataloging, an
alyzing,and short-te
rm storageof their
data) but are not satis
fied with long-term
searching for, desc
ribing or cataloging, an
alyzing,and short-te
rm storageof their
data) but are not satis
fied with long-term
data preservatio
n. Many organization
s do not provide support
to their researchers
for datamanagem
ent bothin the short-
data preservatio
n. Many organization
s do not provide support
to their researchers
for datamanagem
ent bothin the short-
and long-term. If certa
in conditions are m
et (suchas formal citatio
n and sharingreprints)
respondents agr
ee theyare willin
g
and long-term. If certa
in conditions are m
et (suchas formal citatio
n and sharingreprints)
respondents agr
ee theyare willin
g
to share their data. There
are also significant differ
ences and approac
hes in data management prac
tices based on primary
to share their data. There
are also significant differ
ences and approac
hes in data management prac
tices based on primary
fundingagency,
subjectdisciplin
e, age, work focus, an
d world region.
fundingagency,
subjectdisciplin
e, age, work focus, an
d world region.
Conclusions/Sign
ificance:
Conclusions/Sign
ificance:Barriers
to effectivedata sha
ring and preservation are deeply r
ooted in the practices and culture
Barriersto effective
data sharing and preserva
tion are deeply rooted in the practice
s and culture
of the researchprocess
as well as the research
ers themselves. N
ew mandatesfor data
management plan
s from NSF and
of the researchprocess
as well as the research
ers themselves. N
ew mandatesfor data
management plan
s from NSF and
other federal ag
encies and world-w
ide attention to the need to share and preserve
data could lead to changes
. Large scale
other federal ag
encies and world-w
ide attention to the need to share and preserve
data could lead to changes
. Large scale
programs, such as the NSF-spo
nsored DataNET(includin
g projectslike DataON
E) will both bring attentio
n and resources to
programs, such as the NSF-spo
nsored DataNET(includin
g projectslike DataON
E) will both bring attentio
n and resources to
the issue and make it easierfor scien
tists to apply sound data management prin
ciples.
the issue and make it easierfor scien
tists to apply sound data management prin
ciples.
Citation: Tenop
ir C, Allard S, Doug
lass K, Aydinoglu AU, Wu L, et al.
(2011) Data Sharing
by Scientists: Practi
ces and Perceptions. PLo
S ONE 6(6): e21101.
doi:10.1371/jour
nal.pone.0021101
Editor:Cameron Neylon,
Scienceand Technol
ogy FacilitiesCouncil,
United Kingdom
Received January
3, 2011;Accept
ed May 20, 2011; Publis
hed June 29, 2011
Copyright: ! 2011 Tenopir
et al. This is an open-ac
cess article distributed under th
e terms of the CreativeCommons Attribut
ion License,which permits
unrestricted use, dist
ribution, and reprodu
ction in any medium, provided the original
author and source are credited
.
Funding: The p
roject was funde
d as part of the Na
tional Science Fo
undation, Division
of Cyberinfrastru
cture, Data Obse
rvation Networkfor Earth
(DataONE) NSF
award #0830944under a
Cooperative Agre
ement. Thefunders
had no role in study design, dat
a collection and analysis,
decisionto publish,
or preparation of the
manuscript.
CompetingInteres
ts: Theauthors
have declaredthat no
competing interestsexist.
* E-mail: ctenopir@ut
k.edu
Introduction
Data are the inf
rastructure of sci
ence. Sound data are
critical as
they form the basi
s for good scientific
decisions, wise m
anagement
and use of resources, an
d informed decision-making. M
oreover,
‘‘scienceis becom
ing data intensive and collabor
ative’’ [1]. The
amount ofdata collected
, analyzed, re-a
nalyzed, and stored has
increased enormously due to develop
ments in computational
simulationand modeling,
automated data acquisition, and
communication technolo
gies [2].Followin
g the previousresearch
paradigms (experim
ental, theoretical, and computation
al), this
new era has been called ‘‘the fourth paradigm: data-int
ensive
scientificdiscover
y’’ where ‘‘all of t
he scienceliteratur
e is online,
all of the science
data is online, and they interope
rate with each
other’’ [3]. Digi
tal dataare not only
the outputsof resea
rch but
provideinputs t
o new hypotheses, ena
bling new scientificinsights
and drivinginnovati
on [4].
As science becom
es more dataintensiv
e and collaborative, da
ta
sharingbecomes more important.
Data sharingincludes
the
deposition and preserva
tion of data; however, it is primarily
associated with providin
g access for use and reuse of data.
Data
sharinghas many advanta
ges, including:
N re-analysis of da
ta helpsverify re
sults data, which
is a keypart
of the scientificprocess;
N different interpre
tationsor approac
hes to existingdata
contribute to scientific
progress–especia
lly in an interdisci-
plinarysetting;
N well-managed,long-ter
m preservation helps retain
data
integrity;
N when data is available, (re-)c
ollectionof data
is minimized;
thus, use of resou
rces is optimized;
N data availability provide
s safeguards against
misconduct
relatedto data fabricati
on and falsification;
N replication studies s
erve as training
tools fornew generati
ons of
researchers [5][6
][7]
Additionally, res
earchers, data managers
and publishers in the
PARSEsurvey o
verwhelmingly ag
reed that public fundi
ng was the
PLoS ONE | www.plosone.o
rg
1
June 2011 | Volume 6 | Issue
6 | e21101
6
Main Library, Open Access
Open Access via Repository (Green Road)
+ deposit a copy on ZORA
zora.uzh.ch
Publisher researchers
publish a paper in a traditional journal
Publisher
from other researchers in the field were denied [16]. These results
do not include other data practices which may also negatively
affect the progress of science, such as significant delays in the
fulfillment of requests, refusals to publicly present research
findings, and the failure to discuss research with others [16].
Disciplines or subdisciplines have their own culture of data-
sharing. Some do better (geophysics, biodiversity, and astronomy)
than others [17].
Individual Choice vs. Institutional Policies
The extent to which researchers share or withhold data is not
primarily an individual choice. Underlying policies and practices
have great influence on encouraging or inhibiting data sharing.
Several researchers who failed to share their data in the study by
Savage and Vickers, et al., claimed that it would take too much
work to provide raw data. The authors came to the conclusion that
researchers often fail to develop clear, well-annotated datasets to
accompany their research (i.e., metadata), and may lose access and
understanding of the original dataset over time. Vickers, et al.
believe that a policy that would require authors to submit datasets
to journals or public repositories at the time of publication would
help to prevent this occurrence [11]. PARSE Insight, a project
concerned with the preservation of digital information in research,
reported from a survey of data managers that 64% claimed their
organizations had policies and procedures in place to determine
what kinds of data are accepted for storage and preservation, with
specific policies for the time frame and method of submission.
Though this number constitutes a majority, 32% reported a lack of
such policies or procedures [8].Policies and procedures sometimes serve as an active rather
than passive barrier to data sharing. Campbell et al. (2003)
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
[5] [19].
Table 2. Subject discipline.
Frequency Percent
environmental sciences & ecology475
36.1
social sciences
20415.5
biology
18113.7
physical sciences
15812.0
computer science/engineering118
9.0
other
987.4
atmospheric science
523.9
medicine
312.4
Total
1317100.0
doi:10.1371/journal.pone.0021101.t002
Table 3. Data access.
Frequency Percent
An organization-specific system
35138.5%
Long-tem Ecological Research Network292
32.1%
Other data access
24627.0%
A Distributed Active-Archive Center
17319.0%
A Global Biodiversity Information Facility73
8.0%
National Biological Information Infrastructure70
7.7%
National Ecological Observatory Network64
7.0%
International Long-term Ecological Research Network 586.4%
Taiwan Ecological Research Network
7.8%
South African Environmental Observation Network 6.7%
doi:10.1371/journal.pone.0021101.t003
Table 4. Data types.
ResponsesPercent
Experimental
71154.6%
Observational
63248.5%
Data Models
49938.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
Table 1. Primary work sector.
FrequencyPercent
Academic1058
80.5
Government167
12.7
Commercial34
2.6
Non-profit35
2.7
Other
21
1.6
Total
1315
100.0doi:10.1371/journal.pone.0021101.t001
Data Sharing by Scientists
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
from other researchers in the field were denied [16]. These results
do not include other data practices which may also negatively
affect the progress of science, such as significant delays in the
fulfillment of requests, refusals to publicly present research
findings, and the failure to discuss research with others [16].
Disciplines or subdisciplines have their own culture of data-
sharing. Some do better (geophysics, biodiversity, and astronomy)The extent to which researchers share or withhold data is not
primarily an individual choice. Underlying policies and practices
have great influence on encouraging or inhibiting data sharing.
Several researchers who failed to share their data in the study by
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
[5] [19].
Table 4. Data types.
Experimental
71154.6%
Experimental
71154.6%
Observational
63248.5%
Observational
63248.5%
Data Models
49938.3%
Data Models
49938.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Interviews
19515.0%
Other
80
6.1%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
most important reason for data preservation. Nearly all (98%) ofparticipants agreed that if research is publicly funded, the resultsshould become public property and therefore properly preserved[8].This article reports the results of a survey of scientists’ current
data sharing practices and their perceptions of the barriers andenablers of data sharing. The survey was conducted by theresearch team of the National Science Foundation-fundedDataONE project. DataNet supports short- and long-term datamanagement and open access to data. DataONE is one of theinitially funded NSF DataNet partners. DataONE is a large scalecollaboration to develop an organization that supports the fullinformation lifecycle of biological, ecological, and environmentaldata and tools to be used by researchers, educators, students,decision-makers and the general public. DataONE ‘‘will ensurethe preservation and access to multi-scale, multi-discipline, andmulti-national science data’’ [9] by developing a strong cyberin-frastructure and community engagement programs.DataONE will (i) provide coordinated access to current data
collections; (ii) create a new global cyberinfrastructure thatcontains both biological and environmental data coming fromdifferent resources (research networks, environmental observato-ries, individual scientists, and citizen scientists); and (iii) change thescience culture and institutions by providing education andtraining, engaging citizens in science, and building globalcommunities of practice. In order to facilitate change of thescience culture through cyberinfrastructure for data, it is necessaryto first understand the culture of modern science and the role ofdata in it.
Data SharingEncouraging data sharing and reuse begins with good data
practices in all phases of the data lifecycle such as generating andcollecting the data, managing the data, analyzing the data, andsharing it. However, the data lifecycle cannot be consideredindependently from research lifecycle [10], as data are anindispensible element of scientific research. (See Figure 1.)The specific costs of handling supplementary materials such as
datasets are not well documented. In a recent survey, only authorfees and journal subscription fees were mentioned as currentfunding sources for supplementary materials in journals. Partic-ipants in the survey suggested other potential sources for funding,in particular government funding, support from learned societies,and publishers [11].
Data Sharing/Withholding PracticesData sharing is important. According to a study done by
Publishing Research Consortium (PRC) in 2010 with 3823respondents, access to datasets, data models, and algorithms &programs was ranked important or highly important; however,only 38% of them felt that they were easily accessible [12]. Inaddition, it was the lowest among the other information types(some of them were research articles in journals, reference works,technical information, patent information, etc.). Several previoussurveys have explored the benefits and barriers of sharing data[13] and the extent to which researchers share or withhold data.Results seem to suggest that current sharing practices are minimal,although the amount of data sharing varies among different fields.Some journals have specific guidelines which require authors toshare their data with other researchers. However, the extent towhich these guidelines are carried out remains largely untested.Savage and Vickers requested data from ten researchers who hadpublished articles in PLoS journals, which have specific datasharing policies. Only one author sent an original dataset [14].
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subjectdiscipline.Researchers who choose to withhold datasets often have specific
reasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
PublisherPublisher
most important reason for data preservation. Nearly all (98%) ofparticipants agreed that if research is publicly funded, the resultsshould become public property and therefore properly preserved[8].This article reports the results of a survey of scientists’ current
data sharing practices and their perceptions of the barriers andenablers of data sharing. The survey was conducted by theresearch team of the National Science Foundation-fundedDataONE project. DataNet supports short- and long-term datamanagement and open access to data. DataONE is one of theinitially funded NSF DataNet partners. DataONE is a large scalecollaboration to develop an organization that supports the fullinformation lifecycle of biological, ecological, and environmentaldata and tools to be used by researchers, educators, students,decision-makers and the general public. DataONE ‘‘will ensurethe preservation and access to multi-scale, multi-discipline, andmulti-national science data’’ [9] by developing a strong cyberin-frastructure and community engagement programs.DataONE will (i) provide coordinated access to current data
collections; (ii) create a new global cyberinfrastructure thatcontains both biological and environmental data coming fromdifferent resources (research networks, environmental observato-ries, individual scientists, and citizen scientists); and (iii) change thescience culture and institutions by providing education andtraining, engaging citizens in science, and building globalcommunities of practice. In order to facilitate change of thescience culture through cyberinfrastructure for data, it is necessaryto first understand the culture of modern science and the role ofdata in it.
Data SharingEncouraging data sharing and reuse begins with good data
practices in all phases of the data lifecycle such as generating andcollecting the data, managing the data, analyzing the data, andsharing it. However, the data lifecycle cannot be consideredindependently from research lifecycle [10], as data are anindispensible element of scientific research. (See Figure 1.)The specific costs of handling supplementary materials such as
datasets are not well documented. In a recent survey, only authorfees and journal subscription fees were mentioned as currentfunding sources for supplementary materials in journals. Partic-ipants in the survey suggested other potential sources for funding,in particular government funding, support from learned societies,and publishers [11].
Data Sharing/Withholding PracticesData sharing is important. According to a study done by
Publishing Research Consortium (PRC) in 2010 with 3823respondents, access to datasets, data models, and algorithms &programs was ranked important or highly important; however,only 38% of them felt that they were easily accessible [12]. Inaddition, it was the lowest among the other information types(some of them were research articles in journals, reference works,technical information, patent information, etc.). Several previoussurveys have explored the benefits and barriers of sharing data[13] and the extent to which researchers share or withhold data.Results seem to suggest that current sharing practices are minimal,although the amount of data sharing varies among different fields.Some journals have specific guidelines which require authors toshare their data with other researchers. However, the extent towhich these guidelines are carried out remains largely untested.Savage and Vickers requested data from ten researchers who hadpublished articles in PLoS journals, which have specific datasharing policies. Only one author sent an original dataset [14].
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subjectdiscipline.Researchers who choose to withhold datasets often have specific
reasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
Data Sharingby Scientis
ts: Practices and
Perceptions
Carol Tenopir
1*, SuzieAllard
1, Kimberly Douglass
1, ArsevUmur Ayd
inoglu1, Lei W
u1, Eleano
r Read2,
MaribethManoff
2, Mike Frame3
1 School of Inform
ation Sciences, Univers
ity of Tennessee, Kn
oxville, Tennesse
e, United States o
f America, 2Universi
ty of Tennessee Libraries
, University of Tenn
essee,
Knoxville, Tennes
see, United States o
f America, 3Center f
or Biological Inf
ormatics, United States G
eological Survey
, Oak Ridge, Tennesse
e, United States o
f America
Abstract
Background: Scie
ntific research in the 21st cen
tury is more data inte
nsive and collaborative than in the past. It i
s important
to study the data practice
s of researchers
– data accessibility, disc
overy, re-use, pre
servation and, par
ticularly, data sharing.
Data sharing is a valu
able part of the
scientificmethod allowing
for verification of result
s and extending research
from prior
results.
Methodology/Princ
ipal Findings: A
total of1329 scientist
s participated in this survey explorin
g currentdata sharing
practices and percepti
ons of the barriersand enablers
of datasharing.
Scientists do not make their da
ta electronically
availableto others fo
r variousreasons,
including insufficie
nt time and lack of funding. Most resp
ondentsare satisfied
with
their current pro
cesses for the initial an
d short-term parts of
the data or research lifecycle
(collecting their res
earch data;
searching for, desc
ribing or cataloging, an
alyzing,and short-te
rm storageof their
data) but are not satis
fied with long-term
data preservatio
n. Many organization
s do not provide support
to their researchers
for datamanagem
ent bothin the short-
and long-term. If certa
in conditions are m
et (suchas formal citatio
n and sharingreprints)
respondents agr
ee theyare willin
g
to share their data. There
are also significant differ
ences and approac
hes in data management prac
tices based on primary
fundingagency,
subjectdisciplin
e, age, work focus, an
d world region.
Conclusions/Sign
ificance:Barriers
to effectivedata sha
ring and preservation are deeply r
ooted in the practices and culture
of the researchprocess
as well as the research
ers themselves. N
ew mandatesfor data
management plan
s from NSF and
other federal ag
encies and world-w
ide attention to the need to share and preserve
data could lead to changes
. Large scale
programs, such as the NSF-spo
nsored DataNET(includin
g projectslike DataON
E) will both bring attentio
n and resources to
the issue and make it easierfor scien
tists to apply sound data management prin
ciples.
Citation: Tenop
ir C, Allard S, Doug
lass K, Aydinoglu AU, Wu L, et al.
(2011) Data Sharing
by Scientists: Practi
ces and Perceptions. PLo
S ONE 6(6): e21101.
doi:10.1371/jour
nal.pone.0021101
Editor:Cameron Neylon,
Scienceand Technol
ogy FacilitiesCouncil,
United Kingdom
Received January
3, 2011;Accept
ed May 20, 2011; Publis
hed June 29, 2011
Copyright: ! 2011 Tenopir
et al. This is an open-ac
cess article distributed under th
e terms of the CreativeCommons Attribut
ion License,which permits
unrestricted use, dist
ribution, and reprodu
ction in any medium, provided the original
author and source are credited
.
Funding: The p
roject was funde
d as part of the Na
tional Science Fo
undation, Division
of Cyberinfrastru
cture, Data Obse
rvation Networkfor Earth
(DataONE) NSF
award #0830944under a
Cooperative Agre
ement. Thefunders
had no role in study design, dat
a collection and analysis,
decisionto publish,
or preparation of the
manuscript.
CompetingInteres
ts: Theauthors
have declaredthat no
competing interestsexist.
* E-mail: ctenopir@ut
k.edu
Introduction
Data are the inf
rastructure of sci
ence. Sound data are
critical as
they form the basi
s for good scientific
decisions, wise m
anagement
and use of resources, an
d informed decision-making. M
oreover,
‘‘scienceis becom
ing data intensive and collabor
ative’’ [1]. The
amount ofdata collected
, analyzed, re-a
nalyzed, and stored has
increased enormously due to develop
ments in computational
simulationand modeling,
automated data acquisition, and
communication technolo
gies [2].Followin
g the previousresearch
paradigms (experim
ental, theoretical, and computation
al), this
new era has been called ‘‘the fourth paradigm: data-int
ensive
scientificdiscover
y’’ where ‘‘all of t
he scienceliteratur
e is online,
all of the science
data is online, and they interope
rate with each
other’’ [3]. Digi
tal dataare not only
the outputsof resea
rch but
provideinputs t
o new hypotheses, ena
bling new scientificinsights
and drivinginnovati
on [4].
As science becom
es more dataintensiv
e and collaborative, da
ta
sharingbecomes more important.
Data sharingincludes
the
deposition and preserva
tion of data; however, it is primarily
associated with providin
g access for use and reuse of data.
Data
sharinghas many advanta
ges, including:
N re-analysis of da
ta helpsverify re
sults data, which
is a keypart
of the scientificprocess;
N different interpre
tationsor approac
hes to existingdata
contribute to scientific
progress–especia
lly in an interdisci-
plinarysetting;
N well-managed,long-ter
m preservation helps retain
data
integrity;
N when data is available, (re-)c
ollectionof data
is minimized;
thus, use of resou
rces is optimized;
N data availability provide
s safeguards against
misconduct
relatedto data fabricati
on and falsification;
N replication studies s
erve as training
tools fornew generati
ons of
researchers [5][6
][7]
Additionally, res
earchers, data managers
and publishers in the
PARSEsurvey o
verwhelmingly ag
reed that public fundi
ng was the
PLoS ONE | www.plosone.o
rg
1
June 2011 | Volume 6 | Issue
6 | e21101
7
Main Library, Open Access
Open Access via Publisher (Golden Road)
researchers
from other researchers in the field were denied [16]. These results
do not include other data practices which may also negatively
affect the progress of science, such as significant delays in the
fulfillment of requests, refusals to publicly present research
findings, and the failure to discuss research with others [16].
Disciplines or subdisciplines have their own culture of data-
sharing. Some do better (geophysics, biodiversity, and astronomy)
than others [17].
Individual Choice vs. Institutional Policies
The extent to which researchers share or withhold data is not
primarily an individual choice. Underlying policies and practices
have great influence on encouraging or inhibiting data sharing.
Several researchers who failed to share their data in the study by
Savage and Vickers, et al., claimed that it would take too much
work to provide raw data. The authors came to the conclusion that
researchers often fail to develop clear, well-annotated datasets to
accompany their research (i.e., metadata), and may lose access and
understanding of the original dataset over time. Vickers, et al.
believe that a policy that would require authors to submit datasets
to journals or public repositories at the time of publication would
help to prevent this occurrence [11]. PARSE Insight, a project
concerned with the preservation of digital information in research,
reported from a survey of data managers that 64% claimed their
organizations had policies and procedures in place to determine
what kinds of data are accepted for storage and preservation, with
specific policies for the time frame and method of submission.
Though this number constitutes a majority, 32% reported a lack of
such policies or procedures [8].Policies and procedures sometimes serve as an active rather
than passive barrier to data sharing. Campbell et al. (2003)
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
[5] [19].
Table 2. Subject discipline.
Frequency Percent
environmental sciences & ecology475
36.1
social sciences
20415.5
biology
18113.7
physical sciences
15812.0
computer science/engineering118
9.0
other
987.4
atmospheric science
523.9
medicine
312.4
Total
1317100.0
doi:10.1371/journal.pone.0021101.t002
Table 3. Data access.
Frequency Percent
An organization-specific system
35138.5%
Long-tem Ecological Research Network292
32.1%
Other data access
24627.0%
A Distributed Active-Archive Center
17319.0%
A Global Biodiversity Information Facility73
8.0%
National Biological Information Infrastructure70
7.7%
National Ecological Observatory Network64
7.0%
International Long-term Ecological Research Network 586.4%
Taiwan Ecological Research Network
7.8%
South African Environmental Observation Network 6.7%
doi:10.1371/journal.pone.0021101.t003
Table 4. Data types.
ResponsesPercent
Experimental
71154.6%
Observational
63248.5%
Data Models
49938.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
Table 1. Primary work sector.
FrequencyPercent
Academic1058
80.5
Government167
12.7
Commercial34
2.6
Non-profit35
2.7
Other
21
1.6
Total
1315
100.0doi:10.1371/journal.pone.0021101.t001
Data Sharing by Scientists
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
from other researchers in the field were denied [16]. These results
do not include other data practices which may also negatively
affect the progress of science, such as significant delays in the
fulfillment of requests, refusals to publicly present research
findings, and the failure to discuss research with others [16].
Disciplines or subdisciplines have their own culture of data-
sharing. Some do better (geophysics, biodiversity, and astronomy)The extent to which researchers share or withhold data is not
primarily an individual choice. Underlying policies and practices
have great influence on encouraging or inhibiting data sharing.
Several researchers who failed to share their data in the study by
reported that government agencies often have strict policies about
secrecy for some publicly funded research. In a survey of 79
technology transfer officers in American universities, 93%
reported that their institution had a formal policy that required
researchers to file an invention disclosure before seeking to
commercialize research results. About one-half of the participants
reported institutional policies that prohibited the dissemination of
biomaterials without a material transfer agreement, which have
become so complex and demanding that they inhibit sharing [15].
Increasing the efficiency of current data practices in a world of
increased data challenges requires a new comprehensive approach
to data policy and practice. This approach would seek to avoid
data loss, data deluge, poor data practices, scattered data, etc., and
thus make better use of (public) funds and resources. NSF recently
took action by announcing that all proposals to NSF involving
data collection must include a data management plan [1] so that
‘‘digital data are routinely deposited in well-documented form, are
regularly and easily consulted and analyzed by specialist and non-
specialist alike, are openly accessible while suitably protected, and
are reliably preserved’’ [18]. Similarly, the European Commission
invited its member states to develop policies to implement access,
dissemination, and preservation for scientific knowledge and data
[5] [19].
Table 4. Data types.
Experimental
71154.6%
Experimental
71154.6%
Observational
63248.5%
Observational
63248.5%
Data Models
49938.3%
Data Models
49938.3%
Biotic Surveys
44634.3%
Biotic Surveys
44634.3%
Abiotic Surveys442
33.9%
Abiotic Surveys442
33.9%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Abiotic358
27.5%
Remote-Sensed Biotic264
20.3%
Remote-Sensed Biotic264
20.3%
Social Science Surveys251
19.3%
Social Science Surveys251
19.3%
Interviews
19515.0%
Interviews
19515.0%
Other
80
6.1%
Other
80
6.1%
doi:10.1371/journal.pone.0021101.t004
PLoS ONE | www.plosone.org
3
June 2011 | Volume 6 | Issue 6 | e21101
most important reason for data preservation. Nearly all (98%) ofparticipants agreed that if research is publicly funded, the resultsshould become public property and therefore properly preserved[8].This article reports the results of a survey of scientists’ current
data sharing practices and their perceptions of the barriers andenablers of data sharing. The survey was conducted by theresearch team of the National Science Foundation-fundedDataONE project. DataNet supports short- and long-term datamanagement and open access to data. DataONE is one of theinitially funded NSF DataNet partners. DataONE is a large scalecollaboration to develop an organization that supports the fullinformation lifecycle of biological, ecological, and environmentaldata and tools to be used by researchers, educators, students,decision-makers and the general public. DataONE ‘‘will ensurethe preservation and access to multi-scale, multi-discipline, andmulti-national science data’’ [9] by developing a strong cyberin-frastructure and community engagement programs.DataONE will (i) provide coordinated access to current data
collections; (ii) create a new global cyberinfrastructure thatcontains both biological and environmental data coming fromdifferent resources (research networks, environmental observato-ries, individual scientists, and citizen scientists); and (iii) change thescience culture and institutions by providing education andtraining, engaging citizens in science, and building globalcommunities of practice. In order to facilitate change of thescience culture through cyberinfrastructure for data, it is necessaryto first understand the culture of modern science and the role ofdata in it.
Data SharingEncouraging data sharing and reuse begins with good data
practices in all phases of the data lifecycle such as generating andcollecting the data, managing the data, analyzing the data, andsharing it. However, the data lifecycle cannot be consideredindependently from research lifecycle [10], as data are anindispensible element of scientific research. (See Figure 1.)The specific costs of handling supplementary materials such as
datasets are not well documented. In a recent survey, only authorfees and journal subscription fees were mentioned as currentfunding sources for supplementary materials in journals. Partic-ipants in the survey suggested other potential sources for funding,in particular government funding, support from learned societies,and publishers [11].
Data Sharing/Withholding PracticesData sharing is important. According to a study done by
Publishing Research Consortium (PRC) in 2010 with 3823respondents, access to datasets, data models, and algorithms &programs was ranked important or highly important; however,only 38% of them felt that they were easily accessible [12]. Inaddition, it was the lowest among the other information types(some of them were research articles in journals, reference works,technical information, patent information, etc.). Several previoussurveys have explored the benefits and barriers of sharing data[13] and the extent to which researchers share or withhold data.Results seem to suggest that current sharing practices are minimal,although the amount of data sharing varies among different fields.Some journals have specific guidelines which require authors toshare their data with other researchers. However, the extent towhich these guidelines are carried out remains largely untested.Savage and Vickers requested data from ten researchers who hadpublished articles in PLoS journals, which have specific datasharing policies. Only one author sent an original dataset [14].
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subjectdiscipline.Researchers who choose to withhold datasets often have specific
reasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
most important reason for data preservation. Nearly all (98%) ofparticipants agreed that if research is publicly funded, the resultsshould become public property and therefore properly preserved[8].This article reports the results of a survey of scientists’ current
data sharing practices and their perceptions of the barriers andenablers of data sharing. The survey was conducted by theresearch team of the National Science Foundation-fundedDataONE project. DataNet supports short- and long-term datamanagement and open access to data. DataONE is one of theinitially funded NSF DataNet partners. DataONE is a large scalecollaboration to develop an organization that supports the fullinformation lifecycle of biological, ecological, and environmentaldata and tools to be used by researchers, educators, students,decision-makers and the general public. DataONE ‘‘will ensurethe preservation and access to multi-scale, multi-discipline, andmulti-national science data’’ [9] by developing a strong cyberin-frastructure and community engagement programs.DataONE will (i) provide coordinated access to current data
collections; (ii) create a new global cyberinfrastructure thatcontains both biological and environmental data coming fromdifferent resources (research networks, environmental observato-ries, individual scientists, and citizen scientists); and (iii) change thescience culture and institutions by providing education andtraining, engaging citizens in science, and building globalcommunities of practice. In order to facilitate change of thescience culture through cyberinfrastructure for data, it is necessaryto first understand the culture of modern science and the role ofdata in it.
Data SharingEncouraging data sharing and reuse begins with good data
practices in all phases of the data lifecycle such as generating andcollecting the data, managing the data, analyzing the data, andsharing it. However, the data lifecycle cannot be consideredindependently from research lifecycle [10], as data are anindispensible element of scientific research. (See Figure 1.)The specific costs of handling supplementary materials such as
datasets are not well documented. In a recent survey, only authorfees and journal subscription fees were mentioned as currentfunding sources for supplementary materials in journals. Partic-ipants in the survey suggested other potential sources for funding,in particular government funding, support from learned societies,and publishers [11].
Data Sharing/Withholding PracticesData sharing is important. According to a study done by
Publishing Research Consortium (PRC) in 2010 with 3823respondents, access to datasets, data models, and algorithms &programs was ranked important or highly important; however,only 38% of them felt that they were easily accessible [12]. Inaddition, it was the lowest among the other information types(some of them were research articles in journals, reference works,technical information, patent information, etc.). Several previoussurveys have explored the benefits and barriers of sharing data[13] and the extent to which researchers share or withhold data.Results seem to suggest that current sharing practices are minimal,although the amount of data sharing varies among different fields.Some journals have specific guidelines which require authors toshare their data with other researchers. However, the extent towhich these guidelines are carried out remains largely untested.Savage and Vickers requested data from ten researchers who hadpublished articles in PLoS journals, which have specific datasharing policies. Only one author sent an original dataset [14].
Although drawn from a small sample of researchers, these resultsstrongly suggest that journal policies which require data sharing donot necessarily lead authors to make their datasets readily availableto other researchers. The amount of data sharing or data hoardingalso appears to vary according to the researcher’s subjectdiscipline.Researchers who choose to withhold datasets often have specific
reasons for doing so. Savage and Vickers noted reasons thatinclude concerns about patient privacy (for medical fields),concerns about future publishing opportunities, and the desire toretain exclusive rights to data that had taken many years toproduce [14]. In Campbell’s study of data sharing in genetics, thetop reasons cited for withholding data were the amount of effortinvolved in accessing and sharing datasets and the protection of acolleague’s or their own ability to publish [15]. The decision toshare or withhold data is often dependent upon the point of timein the publishing process at which the request is made. Campbell(2003) reported that nearly all (98.7%) of the technology transferofficers surveyed agreed that academic scientists should freelyshare data with other scientists after publication, while only 30.5%agreed that scientists should share data and materials beforepublication. The vast majority also believed that scientists shouldbe more careful when sharing data with industry than with otheracademics [15]. The PARSE Insight survey indicated thatresearchers who are reluctant to share data with others reportedmajor concerns with legal issues, misuse of data, and incompatibledata types [8]. In a survey of geneticists and other life scientists,Campbell et al., found that withholding data may be morecommon in genetics and related fields. Reasons may include theincreased scientific competitiveness of the field, as well as theopportunities for commercial applications. Respondents of thesurvey estimated that ten percent of their requests for information
Figure 1. Joint Information Systems Committee (JISC), Stagesof the research and data lifecycle.doi:10.1371/journal.pone.0021101.g001
Data Sharing by Scientists
PLoS ONE | www.plosone.org 2 June 2011 | Volume 6 | Issue 6 | e21101
Data Sharingby Scientis
ts: Practices and
Perceptions
Carol Tenopir
1*, SuzieAllard
1, Kimberly Douglass
1, ArsevUmur Ayd
inoglu1, Lei W
u1, Eleano
r Read2,
MaribethManoff
2, Mike Frame3
1 School of Inform
ation Sciences, Univers
ity of Tennessee, Kn
oxville, Tennesse
e, United States o
f America, 2Universi
ty of Tennessee Libraries
, University of Tenn
essee,
Knoxville, Tennes
see, United States o
f America, 3Center f
or Biological Inf
ormatics, United States G
eological Survey
, Oak Ridge, Tennesse
e, United States o
f America
Abstract
Background: Scie
ntific research in the 21st cen
tury is more data inte
nsive and collaborative than in the past. It i
s important
to study the data practice
s of researchers
– data accessibility, disc
overy, re-use, pre
servation and, par
ticularly, data sharing.
Data sharing is a valu
able part of the
scientificmethod allowing
for verification of result
s and extending research
from prior
results.
Methodology/Princ
ipal Findings: A
total of1329 scientist
s participated in this survey explorin
g currentdata sharing
practices and percepti
ons of the barriersand enablers
of datasharing.
Scientists do not make their da
ta electronically
availableto others fo
r variousreasons,
including insufficie
nt time and lack of funding. Most resp
ondentsare satisfied
with
their current pro
cesses for the initial an
d short-term parts of
the data or research lifecycle
(collecting their res
earch data;
searching for, desc
ribing or cataloging, an
alyzing,and short-te
rm storageof their
data) but are not satis
fied with long-term
data preservatio
n. Many organization
s do not provide support
to their researchers
for datamanagem
ent bothin the short-
and long-term. If certa
in conditions are m
et (suchas formal citatio
n and sharingreprints)
respondents agr
ee theyare willin
g
to share their data. There
are also significant differ
ences and approac
hes in data management prac
tices based on primary
fundingagency,
subjectdisciplin
e, age, work focus, an
d world region.
Conclusions/Sign
ificance:Barriers
to effectivedata sha
ring and preservation are deeply r
ooted in the practices and culture
of the researchprocess
as well as the research
ers themselves. N
ew mandatesfor data
management plan
s from NSF and
other federal ag
encies and world-w
ide attention to the need to share and preserve
data could lead to changes
. Large scale
programs, such as the NSF-spo
nsored DataNET(includin
g projectslike DataON
E) will both bring attentio
n and resources to
the issue and make it easierfor scien
tists to apply sound data management prin
ciples.
Citation: Tenop
ir C, Allard S, Doug
lass K, Aydinoglu AU, Wu L, et al.
(2011) Data Sharing
by Scientists: Practi
ces and Perceptions. PLo
S ONE 6(6): e21101.
doi:10.1371/jour
nal.pone.0021101
Editor:Cameron Neylon,
Scienceand Technol
ogy FacilitiesCouncil,
United Kingdom
Received January
3, 2011;Accept
ed May 20, 2011; Publis
hed June 29, 2011
Copyright: ! 2011 Tenopir
et al. This is an open-ac
cess article distributed under th
e terms of the CreativeCommons Attribut
ion License,which permits
unrestricted use, dist
ribution, and reprodu
ction in any medium, provided the original
author and source are credited
.
Funding: The p
roject was funde
d as part of the Na
tional Science Fo
undation, Division
of Cyberinfrastru
cture, Data Obse
rvation Networkfor Earth
(DataONE) NSF
award #0830944under a
Cooperative Agre
ement. Thefunders
had no role in study design, dat
a collection and analysis,
decisionto publish,
or preparation of the
manuscript.
CompetingInteres
ts: Theauthors
have declaredthat no
competing interestsexist.
* E-mail: ctenopir@ut
k.edu
Introduction
Data are the inf
rastructure of sci
ence. Sound data are
critical as
they form the basi
s for good scientific
decisions, wise m
anagement
and use of resources, an
d informed decision-making. M
oreover,
‘‘scienceis becom
ing data intensive and collabor
ative’’ [1]. The
amount ofdata collected
, analyzed, re-a
nalyzed, and stored has
increased enormously due to develop
ments in computational
simulationand modeling,
automated data acquisition, and
communication technolo
gies [2].Followin
g the previousresearch
paradigms (experim
ental, theoretical, and computation
al), this
new era has been called ‘‘the fourth paradigm: data-int
ensive
scientificdiscover
y’’ where ‘‘all of t
he scienceliteratur
e is online,
all of the science
data is online, and they interope
rate with each
other’’ [3]. Digi
tal dataare not only
the outputsof resea
rch but
provideinputs t
o new hypotheses, ena
bling new scientificinsights
and drivinginnovati
on [4].
As science becom
es more dataintensiv
e and collaborative, da
ta
sharingbecomes more important.
Data sharingincludes
the
deposition and preserva
tion of data; however, it is primarily
associated with providin
g access for use and reuse of data.
Data
sharinghas many advanta
ges, including:
N re-analysis of da
ta helpsverify re
sults data, which
is a keypart
of the scientificprocess;
N different interpre
tationsor approac
hes to existingdata
contribute to scientific
progress–especia
lly in an interdisci-
plinarysetting;
N well-managed,long-ter
m preservation helps retain
data
integrity;
N when data is available, (re-)c
ollectionof data
is minimized;
thus, use of resou
rces is optimized;
N data availability provide
s safeguards against
misconduct
relatedto data fabricati
on and falsification;
N replication studies s
erve as training
tools fornew generati
ons of
researchers [5][6
][7]
Additionally, res
earchers, data managers
and publishers in the
PARSEsurvey o
verwhelmingly ag
reed that public fundi
ng was the
PLoS ONE | www.plosone.o
rg
1
June 2011 | Volume 6 | Issue
6 | e21101
publish a paper in a Open Access journal
Publisher
8
Main Library, Open Access
9
Main Library, Open Access
Open Access via publisher (Golden Road)
Solomon and Björk (2012). A study of open access journals using article processing charges. Journal of the American Society for Information Science and Technology. Preprint available at: http://www.openaccesspublishing.org/apc2/.
To publish in Open Access Journals authors sometimes (but not always!) have to pay a fee per article: •! Range of fee: 8 – 3900 USD
•! Average fee: 906 USD
•! 7892 journals in Directory of Open Access Journals (DOAJ)
Some Journals use explicitly: or :
10
Main Library, Open Access
Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities (2003)
Open access contributions include original scientific research results, raw data and metadata, source materials, digital representations of pictorial and graphical materials and scholarly multimedia material.
„
http://oa.mpg.de/lang/de/berlin-prozess/berliner-erklarung/
Our mission of disseminating knowledge is only half complete if the information is not made widely and readily available to society. New possibilities of knowledge dissemination [...] through the open access paradigm via the Internet have to be supported. „
11
Main Library, Open Access
Swiss Signatories of the Berlin Declaration
http://oa.mpg.de/lang/en-uk/berlin-prozess/signatoren/
12
Main Library, Open Access
Guidelines Swiss National Science Foundation (since 2007)
The SNSF requires grantees to provide open access to research results obtained with the help of SNSF grants (Article 44 Funding Regulations).
„
http://www.snf.ch/SiteCollectionDocuments/allg_reglement_valorisierung_e.pdf
13
Main Library, Open Access
Guidelines University of Zurich (since 2008)
The University of Zurich requires their researchers to deposit a copy of all their published scientific works in the Zurich Open Repository and Archive (ZORA) with open access, if there are no legal objections.
„The University of Zurich encourages and supports their authors to publish their research articles in Open Access journals where a suitable journal exists and provides the support to enable that to happen.
„http://www.oai.uzh.ch/en/working-with-zora/regulations/guidelines
14
Main Library, Open Access
35% 38% 44% 44%
15
35% 38% 44% 44% 44% 44%
2008 2009 2010 2011 2012 Closed Access 5235 5167 4708 5123 899 Open Access 2784 3149 3463 3300 424
0
2000
4000
6000
8000
Publications in ZORA (2008 – June 2012)
35% 38% 42% 39%
Open Access includes publications with an embargo and publications which are freely accessible at the publishers website
Main Library, Open Access
Why not 100% Open Access? Some personal observations...
16
Main Library, Open Access
Legal Objections „ Copyright Transfer Statement The author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. The copyright transfer covers the exclusive right and license to reproduce, publish, distribute and archive the article in all forms and media of expression now known or developed in the future, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
Example CTA of Springer: http://www.springer.com/?SGWID=3-102-45-69724-0
17
Main Library, Open Access
Reluctance to share accepted manuscript
Journal of Neuroscience, in press
Section: Cellular/Molecular Neuroscience
Senior Editor: Dr. Gail Mandel
Src-family kinases stabilize the neuromuscular synapse in vivo
via protein interactions, phosphorylation, and cytoskeletal linkage
of acetylcholine receptors
Abbreviated title: Src action in postsynaptic stabilization
Gayathri Sadasivam*, Raffaella Willmann*, Shuo Lin§, Susanne Erb-Vögtli*, Xian Chu Kong§,
Markus A. Rüegg§, and Christian Fuhrer*
*Department of Neurochemistry, Brain Research Institute, University of Zürich,
Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
§Biozentrum, University of Basel, Klingelbergstrasse 70, CH-4056 Basel, Switzerland
Address for correspondence: Christian Fuhrer, Brain Research Institute, University of Zürich,
Winterthurerstrasse 190, CH-8057 Zürich, Switzerland. Tel.: +41 44 635 33 10. Fax: +41 44 635
33 03. E-mail: [email protected]
Number of Figures: 10; 1 Supplementary Figure; Number of pages: 32
Key words: Src, acetylcholine receptor, neuromuscular synapse, agrin, tyrosine-phosphorylation,
postsynaptic membrane
Acknowledgements: We thank Dr. Mathias Höchli and Dr. Anne Greet Bittermann from the
Laboratory of Electron Microscopy at the University of Zürich for their excellent technical
assistance with the confocal microscope. This work was supported by the Eric Slack-Gyr
Foundation, and by grants from the Swiss National Science Foundation, the Swiss Foundation
for Research on Muscle Diseases and the Zürich Neuroscience Center (to C.F.).
1
Cellular/Molecular
Src-Family Kinases Stabilize the Neuromuscular Synapse InVivo via Protein Interactions, Phosphorylation, andCytoskeletal Linkage of Acetylcholine Receptors
Gayathri Sadasivam,1 Raffaella Willmann,1 Shuo Lin,2 Susanne Erb-Vogtli,1 Xian Chu Kong,2 Markus A. Ruegg,2 andChristian Fuhrer1
1Department of Neurochemistry, Brain Research Institute, University of Zurich, CH-8057 Zurich, Switzerland, and 2Biozentrum, University of Basel,CH-4056 Basel, Switzerland
Postnatal stabilization and maturation of the postsynaptic membrane are important for development and function of the neuromuscularjunction (NMJ), but the underlying mechanisms remain poorly characterized. We examined the role of Src-family kinases (SFKs) in vivo.Electroporation of kinase-inactive Src constructs into soleus muscles of adult mice caused NMJ disassembly: acetylcholine receptor(AChR)-rich areas became fragmented; the topology of nerve terminal, AChRs, and synaptic nuclei was disturbed; and occasionallynerves started to sprout. Electroporation of kinase-overactive Src produced similar but milder effects. We studied the mechanism of SFKaction using cultured src!/!;fyn!/! myotubes, focusing on clustering of postsynaptic proteins, their interaction with AChRs, and AChRphosphorylation. Rapsyn and the utrophin-glycoprotein complex were recruited normally into AChR-containing clusters by agrin insrc!/!;fyn!/! myotubes. But after agrin withdrawal, clusters of these proteins disappeared rapidly in parallel with AChRs, revealing thatSFKs are of general importance in postsynaptic stability. At the same time, AChR interaction with rapsyn and dystrobrevin and AChRphosphorylation decreased after agrin withdrawal from mutant myotubes. Unexpectedly, levels of rapsyn protein were increased insrc!/!;fyn!/! myotubes, whereas rapsyn– cytoskeleton interactions were unaffected. The overall cytoskeletal link of AChRs was weakbut still strengthened by agrin in mutant cells, consistent with the normal formation but decreased stability of AChR clusters. These datashow that correctly balanced activity of SFKs is critical in maintaining adult NMJs in vivo. SFKs hold the postsynaptic apparatus togetherthrough stabilization of AChR–rapsyn interaction and AChR phosphorylation. In addition, SFKs control rapsyn levels and AChR-cytoskeletal linkage.
Key words: Src; acetylcholine receptor; neuromuscular synapse; agrin; tyrosine phosphorylation; postsynaptic membrane
IntroductionNeuromuscular junctions (NMJs) develop in a series of steps inwhich the postsynaptic membrane first forms by concentratingacetylcholine receptors (AChRs) and associated proteins in a flattopology. Postnatally, NMJs mature and AChRs get arranged atthe crests of postjunctional folds. Concomitantly, all but oneaxon withdrew, paralleled by destabilization of adjacent AChRs(Sanes and Lichtman, 2001). Maturation and stabilization ofAChR clusters ensure proper synaptic development, which formsthe basis for nerve-evoked muscle contractibility.
Much is known about the molecular pathways that first formNMJs. Neural agrin, by activating the muscle-specific kinase(MuSK), is crucial by triggering downstream cascades (for re-
view, see Bezakova and Ruegg, 2003; Luo et al., 2003). Central inthese is rapsyn, the main AChR-anchoring protein mediatingclustering (Gautam et al., 1995). Rapsyn increasingly binds toAChRs in response to agrin (Moransard et al., 2003), mediatesagrin-induced phosphorylation of the AChR ! and " subunits(Mittaud et al., 2001), and links the receptor to !-dystroglycan, acomponent of the postsynaptic utrophin-glycoprotein complex(UGC) (Cartaud et al., 1998; Bartoli et al., 2001). In clustering,AChRs become immobilized and less detergent extractable, bothin agrin-treated myotubes (Prives et al., 1982; Stya and Axelrod,1983; Podleski and Salpeter, 1988) and developing NMJs(Dennis, 1981; Slater, 1982). The players in this cytoskeletal linkremain uncertain. Agrin-induced phosphorylation of AChR ! isinvolved (Borges and Ferns, 2001) and can occur through Abl-and Src-family kinases (SFKs) (Finn et al., 2003; Mittaud et al.,2004).
Much less is known about the mechanisms that mature NMJsand stabilize AChR clusters postnatally. Although MuSK is re-quired (Kong et al., 2004), some of these pathways may not beessential in initial NMJ formation (Willmann and Fuhrer, 2002),as illustrated by mice lacking utrophin and dystrophin or theUGC components #-dystrobrevin or dystroglycan (Grady et al.,
Received May 25, 2005; revised Sept. 28, 2005; accepted Sept. 29, 2005.This work was supported by the Eric Slack-Gyr Foundation and by grants from the Swiss National Science Foun-
dation, the Swiss Foundation for Research on Muscle Diseases, and the Zurich Neuroscience Center (C.F.). We thankDrs. Mathias Hochli and Anne Greet Bittermann (Laboratory of Electron Microscopy, University of Zurich) for theirexcellent technical assistance with the confocal microscope.
Correspondence should be addressed to Christian Fuhrer, Brain Research Institute, University of Zurich, Winter-thurerstrasse 190, CH-8057 Zurich, Switzerland. E-mail: [email protected].
DOI:10.1523/JNEUROSCI.2103-05.2005Copyright © 2005 Society for Neuroscience 0270-6474/05/2510479-15$15.00/0
The Journal of Neuroscience, November 9, 2005 • 25(45):10479 –10493 • 10479
Accepted manuscript (Post-Print) Published PDF
Department of Neurochemistry, Brain Research Institute, University of Zu ¨rich, Switzerland, and
Postnatal stabilization and maturation of the postsynaptic membrane are important for development and function of the neuromuscularjunction (NMJ), but the underlying mechanisms remain poorly characterized. We examined the role of Src-family kinases (SFKs)Electroporation of kinase-inactive Src constructs into soleus muscles of adult mice caused NMJ disassembly: acetylcholine receptor(AChR)-rich areas became fragmented; the topology of nerve terminal, AChRs, and synaptic nuclei was disturbed; and occasionallynerves started to sprout. Electroporation of kinase-overactive Src produced similar but milder effects. We studied the mechanism of SFK
;fyn!/! myotubes, focusing on clustering of postsynaptic proteins, their interaction with AChRs, and AChRphosphorylation. Rapsyn and the utrophin-glycoprotein complex were recruited normally into AChR-containing clusters by agrin in
myotubes. But after agrin withdrawal, clusters of these proteins disappeared rapidly in parallel with AChRs, revealing thatSFKs are of general importance in postsynaptic stability. At the same time, AChR interaction with rapsyn and dystrobrevin and AChRphosphorylation decreased after agrin withdrawal from mutant myotubes. Unexpectedly, levels of rapsyn protein were increased in
myotubes, whereas rapsyn– cytoskeleton interactions were unaffected. The overall cytoskeletal link of AChRs was weakbut still strengthened by agrin in mutant cells, consistent with the normal formation but decreased stability of AChR clusters. These datashow that correctly balanced activity of SFKs is critical in maintaining adult NMJs in vivo. SFKs hold the postsynaptic apparatus togetherthrough stabilization of AChR–rapsyn interaction and AChR phosphorylation. In addition, SFKs control rapsyn levels and AChR-
Src; acetylcholine receptor; neuromuscular synapse; agrin; tyrosine phosphorylation; postsynaptic membrane
Neuromuscular junctions (NMJs) develop in a series of steps inwhich the postsynaptic membrane first forms by concentratingacetylcholine receptors (AChRs) and associated proteins in a flattopology. Postnatally, NMJs mature and AChRs get arranged atthe crests of postjunctional folds. Concomitantly, all but oneaxon withdrew, paralleled by destabilization of adjacent AChRs
view, see Bezakova and Ruegg, 2003; Luo et al., 2003). Central inthese is rapsyn, the main AChR-anchoring protein mediatingclustering (Gautam et al., 1995). Rapsyn increasingly binds toAChRs in response to agrin (Moransard et al., 2003), mediatesagrin-induced phosphorylation of the AChR(Mittaud et al., 2001), and links the receptor tocomponent of the postsynaptic utrophin-glycoprotein complex
Image: Charles Le Brun, 1760
18
Main Library, Open Access
Reluctance to share accepted manuscript
Journal of Neuroscience, in press
Section: Cellular/Molecular Neuroscience
Senior Editor: Dr. Gail Mandel
Src-family kinases stabilize the neuromuscular synapse in vivo
via protein interactions, phosphorylation, and cytoskeletal linkage
of acetylcholine receptors
Abbreviated title: Src action in postsynaptic stabilization
Gayathri Sadasivam*, Raffaella Willmann*, Shuo Lin§, Susanne Erb-Vögtli*, Xian Chu Kong§,
Markus A. Rüegg§, and Christian Fuhrer*
*Department of Neurochemistry, Brain Research Institute, University of Zürich,
Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
§Biozentrum, University of Basel, Klingelbergstrasse 70, CH-4056 Basel, Switzerland
Address for correspondence: Christian Fuhrer, Brain Research Institute, University of Zürich,
Winterthurerstrasse 190, CH-8057 Zürich, Switzerland. Tel.: +41 44 635 33 10. Fax: +41 44 635
33 03. E-mail: [email protected]
Number of Figures: 10; 1 Supplementary Figure; Number of pages: 32
Key words: Src, acetylcholine receptor, neuromuscular synapse, agrin, tyrosine-phosphorylation,
postsynaptic membrane
Acknowledgements: We thank Dr. Mathias Höchli and Dr. Anne Greet Bittermann from the
Laboratory of Electron Microscopy at the University of Zürich for their excellent technical
assistance with the confocal microscope. This work was supported by the Eric Slack-Gyr
Foundation, and by grants from the Swiss National Science Foundation, the Swiss Foundation
for Research on Muscle Diseases and the Zürich Neuroscience Center (to C.F.).
1
Cellular/Molecular
Src-Family Kinases Stabilize the Neuromuscular Synapse InVivo via Protein Interactions, Phosphorylation, andCytoskeletal Linkage of Acetylcholine Receptors
Gayathri Sadasivam,1 Raffaella Willmann,1 Shuo Lin,2 Susanne Erb-Vogtli,1 Xian Chu Kong,2 Markus A. Ruegg,2 andChristian Fuhrer1
1Department of Neurochemistry, Brain Research Institute, University of Zurich, CH-8057 Zurich, Switzerland, and 2Biozentrum, University of Basel,CH-4056 Basel, Switzerland
Postnatal stabilization and maturation of the postsynaptic membrane are important for development and function of the neuromuscularjunction (NMJ), but the underlying mechanisms remain poorly characterized. We examined the role of Src-family kinases (SFKs) in vivo.Electroporation of kinase-inactive Src constructs into soleus muscles of adult mice caused NMJ disassembly: acetylcholine receptor(AChR)-rich areas became fragmented; the topology of nerve terminal, AChRs, and synaptic nuclei was disturbed; and occasionallynerves started to sprout. Electroporation of kinase-overactive Src produced similar but milder effects. We studied the mechanism of SFKaction using cultured src!/!;fyn!/! myotubes, focusing on clustering of postsynaptic proteins, their interaction with AChRs, and AChRphosphorylation. Rapsyn and the utrophin-glycoprotein complex were recruited normally into AChR-containing clusters by agrin insrc!/!;fyn!/! myotubes. But after agrin withdrawal, clusters of these proteins disappeared rapidly in parallel with AChRs, revealing thatSFKs are of general importance in postsynaptic stability. At the same time, AChR interaction with rapsyn and dystrobrevin and AChRphosphorylation decreased after agrin withdrawal from mutant myotubes. Unexpectedly, levels of rapsyn protein were increased insrc!/!;fyn!/! myotubes, whereas rapsyn– cytoskeleton interactions were unaffected. The overall cytoskeletal link of AChRs was weakbut still strengthened by agrin in mutant cells, consistent with the normal formation but decreased stability of AChR clusters. These datashow that correctly balanced activity of SFKs is critical in maintaining adult NMJs in vivo. SFKs hold the postsynaptic apparatus togetherthrough stabilization of AChR–rapsyn interaction and AChR phosphorylation. In addition, SFKs control rapsyn levels and AChR-cytoskeletal linkage.
Key words: Src; acetylcholine receptor; neuromuscular synapse; agrin; tyrosine phosphorylation; postsynaptic membrane
IntroductionNeuromuscular junctions (NMJs) develop in a series of steps inwhich the postsynaptic membrane first forms by concentratingacetylcholine receptors (AChRs) and associated proteins in a flattopology. Postnatally, NMJs mature and AChRs get arranged atthe crests of postjunctional folds. Concomitantly, all but oneaxon withdrew, paralleled by destabilization of adjacent AChRs(Sanes and Lichtman, 2001). Maturation and stabilization ofAChR clusters ensure proper synaptic development, which formsthe basis for nerve-evoked muscle contractibility.
Much is known about the molecular pathways that first formNMJs. Neural agrin, by activating the muscle-specific kinase(MuSK), is crucial by triggering downstream cascades (for re-
view, see Bezakova and Ruegg, 2003; Luo et al., 2003). Central inthese is rapsyn, the main AChR-anchoring protein mediatingclustering (Gautam et al., 1995). Rapsyn increasingly binds toAChRs in response to agrin (Moransard et al., 2003), mediatesagrin-induced phosphorylation of the AChR ! and " subunits(Mittaud et al., 2001), and links the receptor to !-dystroglycan, acomponent of the postsynaptic utrophin-glycoprotein complex(UGC) (Cartaud et al., 1998; Bartoli et al., 2001). In clustering,AChRs become immobilized and less detergent extractable, bothin agrin-treated myotubes (Prives et al., 1982; Stya and Axelrod,1983; Podleski and Salpeter, 1988) and developing NMJs(Dennis, 1981; Slater, 1982). The players in this cytoskeletal linkremain uncertain. Agrin-induced phosphorylation of AChR ! isinvolved (Borges and Ferns, 2001) and can occur through Abl-and Src-family kinases (SFKs) (Finn et al., 2003; Mittaud et al.,2004).
Much less is known about the mechanisms that mature NMJsand stabilize AChR clusters postnatally. Although MuSK is re-quired (Kong et al., 2004), some of these pathways may not beessential in initial NMJ formation (Willmann and Fuhrer, 2002),as illustrated by mice lacking utrophin and dystrophin or theUGC components #-dystrobrevin or dystroglycan (Grady et al.,
Received May 25, 2005; revised Sept. 28, 2005; accepted Sept. 29, 2005.This work was supported by the Eric Slack-Gyr Foundation and by grants from the Swiss National Science Foun-
dation, the Swiss Foundation for Research on Muscle Diseases, and the Zurich Neuroscience Center (C.F.). We thankDrs. Mathias Hochli and Anne Greet Bittermann (Laboratory of Electron Microscopy, University of Zurich) for theirexcellent technical assistance with the confocal microscope.
Correspondence should be addressed to Christian Fuhrer, Brain Research Institute, University of Zurich, Winter-thurerstrasse 190, CH-8057 Zurich, Switzerland. E-mail: [email protected].
DOI:10.1523/JNEUROSCI.2103-05.2005Copyright © 2005 Society for Neuroscience 0270-6474/05/2510479-15$15.00/0
The Journal of Neuroscience, November 9, 2005 • 25(45):10479 –10493 • 10479
Accepted manuscript (Post-Print) Published PDF
Department of Neurochemistry, Brain Research Institute, University of Zu ¨rich, Switzerland, and
Postnatal stabilization and maturation of the postsynaptic membrane are important for development and function of the neuromuscularjunction (NMJ), but the underlying mechanisms remain poorly characterized. We examined the role of Src-family kinases (SFKs)Electroporation of kinase-inactive Src constructs into soleus muscles of adult mice caused NMJ disassembly: acetylcholine receptor(AChR)-rich areas became fragmented; the topology of nerve terminal, AChRs, and synaptic nuclei was disturbed; and occasionallynerves started to sprout. Electroporation of kinase-overactive Src produced similar but milder effects. We studied the mechanism of SFK
;fyn!/! myotubes, focusing on clustering of postsynaptic proteins, their interaction with AChRs, and AChRphosphorylation. Rapsyn and the utrophin-glycoprotein complex were recruited normally into AChR-containing clusters by agrin in
myotubes. But after agrin withdrawal, clusters of these proteins disappeared rapidly in parallel with AChRs, revealing thatSFKs are of general importance in postsynaptic stability. At the same time, AChR interaction with rapsyn and dystrobrevin and AChRphosphorylation decreased after agrin withdrawal from mutant myotubes. Unexpectedly, levels of rapsyn protein were increased in
myotubes, whereas rapsyn– cytoskeleton interactions were unaffected. The overall cytoskeletal link of AChRs was weakbut still strengthened by agrin in mutant cells, consistent with the normal formation but decreased stability of AChR clusters. These datashow that correctly balanced activity of SFKs is critical in maintaining adult NMJs in vivo. SFKs hold the postsynaptic apparatus togetherthrough stabilization of AChR–rapsyn interaction and AChR phosphorylation. In addition, SFKs control rapsyn levels and AChR-
Src; acetylcholine receptor; neuromuscular synapse; agrin; tyrosine phosphorylation; postsynaptic membrane
Neuromuscular junctions (NMJs) develop in a series of steps inwhich the postsynaptic membrane first forms by concentratingacetylcholine receptors (AChRs) and associated proteins in a flattopology. Postnatally, NMJs mature and AChRs get arranged atthe crests of postjunctional folds. Concomitantly, all but one
view, see Bezakova and Ruegg, 2003; Luo et al., 2003). Central inthese is rapsyn, the main AChR-anchoring protein mediatingclustering (Gautam et al., 1995). Rapsyn increasingly binds toAChRs in response to agrin (Moransard et al., 2003), mediatesagrin-induced phosphorylation of the AChR(Mittaud et al., 2001), and links the receptor tocomponent of the postsynaptic utrophin-glycoprotein complex
Gayathri Sadasivam,Christian Fuhrer1
1Department of Neurochemistry, Brain Research Institute, University of ZuCH-4056 Basel, Switzerland
Postnatal stabilization and maturation of the postsynaptic membrane are important for development and function of the neuromuscularjunction (NMJ), but the underlying mechanisms remain poorly characterized. We examined the role of Src-family kinases (SFKs)Electroporation of kinase-inactive Src constructs into soleus muscles of adult mice caused NMJ disassembly: acetylcholine receptor(AChR)-rich areas became fragmented; the topology of nerve terminal, AChRs, and synaptic nuclei was disturbed; and occasionallynerves started to sprout. Electroporation of kinase-overactive Src produced similar but milder effects. We studied the mechanism of SFKaction using culturedphosphorylation. Rapsyn and the utrophin-glycoprotein complex were recruited normally into AChR-containing clusters by agrin insrc!/!;fyn!/! myotubes. But after agrin withdrawal, clusters of these proteins disappeared rapidly in parallel with AChRs, revealing thatSFKs are of general importance in postsynaptic stability. At the same time, AChR interaction with rapsyn and dystrobrevin and AChRphosphorylation decreased after agrin withdrawal from mutant myotubes. Unexpectedly, levels of rapsyn protein were increased insrc!/!;fyn!/! myotubes, whereas rapsyn– cytoskeleton interactions were unaffected. The overall cytoskeletal link of AChRs was weakbut still strengthened by agrin in mutant cells, consistent with the normal formation but decreased stability of AChR clusters. These datashow that correctly balanced activity of SFKs is critical in maintaining adult NMJsthrough stabilization of AChR–rapsyn interaction and AChR phosphorylation. In addition, SFKs control rapsyn levels and AChR-cytoskeletal linkage.
Key words: Src; acetylcholine receptor; neuromuscular synapse; agrin; tyrosine phosphorylation; postsynaptic membrane
IntroductionNeuromuscular junctions (NMJs) develop in a series of steps inwhich the postsynaptic membrane first forms by concentratingacetylcholine receptors (AChRs) and associated proteins in a flattopology. Postnatally, NMJs mature and AChRs get arranged atthe crests of postjunctional folds. Concomitantly, all but oneaxon withdrew, paralleled by destabilization of adjacent AChRs(Sanes and Lichtman, 2001). Maturation and stabilization ofAChR clusters ensure proper synaptic development, which formsthe basis for nerve-evoked muscle contractibility.
Much is known about the molecular pathways that first formNMJs. Neural agrin, by activating the muscle-specific kinase(MuSK), is crucial by triggering downstream cascades (for re-
Received May 25, 2005; revised Sept. 28, 2005; accepted Sept. 29, 2005.This work was supported by the Eric Slack-Gyr Foundation and by grants from the Swiss National Science Foun-
dation, the Swiss Foundation for Research on Muscle Diseases, and the ZuDrs. Mathias Hochli and Anne Greet Bittermann (Laboratory of Electron Microscopy, University of ZuDrs. Mathias Hochli and Anne Greet Bittermann (Laboratory of Electron Microscopy, University of ZuDrs. Mathias Hoexcellent technical assistance with the confocal microscope.
Correspondence should be addressed to Christian Fuhrer, Brain Research Institute, University of Zuthurerstrasse 190, CH-8057 Zurich, Switzerland. E-mail: [email protected] 190, CH-8057 Zurich, Switzerland. E-mail: [email protected] 190, CH-8057 Zu
DOI:10.1523/JNEUROSCI.2103-05.2005Copyright © 2005 Society for Neuroscience 0270-6474/05/2510479-15$15.00/0
Abbreviated title: Src action in postsynaptic stabilization
ann*, Shuo Lin§, Susanne Erb-Vögtli*, Xian Chu Kong§,
Institute, University of Zürich,
-8057 Zürich, Switzerland
lbergstrasse 70, CH-4056 Basel, Switzerland
, Brain Research Institute, University of Zürich,
zerland. Tel.: +41 44 635 33 10. Fax: +41 44 635
cular synapse, agrin, tyrosine-phosphorylation,
Höchli and Dr. Anne Greet Bittermann from the
of Zürich for their excellent technical
was supported by the Eric Slack-Gyr
tional Science Foundation, the Swiss Foundation
Zürich Neuroscience Center (to C.F.).
1
manuscript (Post-Print)
Postnatal stabilization and maturation of the postsynaptic membrane are important for development and function of the neuromuscularjunction (NMJ), but the underlying mechanisms remain poorly characterized. We examined the role of Src-family kinases (SFKs)
Sharing a non-final version?
No Way!!
Image: Charles Le Brun, 1760
19
Main Library, Open Access
Assumed conflicts with publishers and editors
„ Ich fürchte schlicht Komplikationen mit den jeweiligen Herausgebern, die ich ja nicht gefragt habe. Ein gutes Verhältnis zu denen ist mir aber wichtig, und das möchte ich nicht aufs Spiel setzen. Seminarleiter, April 2012
20
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Unawareness of the problems (eg. prices)
21
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Academic evaluation and reputation system
22
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No time for Open Access
Photo by Timm Suess
23
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No interest & Ignorance of guidelines
„ Danke für Ihre freundliche Frage. Ich verzichte auf die Präsentation in ZORA. Danke und mit den besten Grüssen Professorin, März 2012
24
Main Library, Open Access
Open Access Funding at the University of Zurich
25
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+ Open Access Publishing Fund
for social sciences and humanities
Memberships
http://www.oai.uzh.ch/en/at-the-uzh/funding/
26
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BioMed Central – 220 Open Access Journals
http://dx.doi.org/10.1186/1756-3305-5-85
27
Main Library, Open Access
Number of BioMed Central publications in ZORA
0 3 5 13 19 21 25
48
92 85
127
162
12
0
20
40
60
80
100
120
140
160
180
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
28
Main Library, Open Access
Year 2011 •! Open Access funding: 162‘000 CHF
•! Journal subscriptions: 4‘061‘000 CHF (only Main Library)
Open Access funding vs. Journal subscriptions
Jahresbericht 2011 der Haupbibliothek Universität Zürich Images: Fly by Domini Li, Wellcome Images, B0004872, Elephant: Meyers Konversationslexikon
29
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Challenge: Who can do what?
Researcher
University
Libraries Publisher
Funder
30
Main Library, Open Access
Who is doing the coordination?
Researcher
University
Libraries Publisher
Funder
31
Main Library, Open Access
European Commisson Main Library University of Zurich is on of 41 project partners in the EU-Project:
Open Access and Open Science expected to be a key part for the upcoming Horizon 2020 research program
http://www.openaire.eu/en/open-access/country-information/switzerland
32
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SCOAP3
33
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Summary
• Open Access is a proven and solid business model • It works for top journals • Open Access is growing slowly, but constantly
• 100% Open Access is not expected to cost less, but there is added value.
• … Open Access to research data is an upcoming topic
34
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BACKUP-SLIDES
Main Library, Open Access
http://gowers.wordpress.com/2012/01/21/elsevier-my-part-in-its-downfall/
Publications
Data
Funding
!Linked Research
?
API
Supporting Open Science in Europe
Who bene� ts from OpenAIRE? EU researchers who access, deposit and link to research output
National Open Access initiatives
Repository managers
Policy makers and funders who monitor funded work
Publishers who wish to raise visibility of output
Potential data providers who want to explore linking up their research
What is OpenAIRE? A Participatory European Open Access infrastructure to manage scientifi c publications and associated information via repository networks
Harvests and indexes FP7 Open Access publications
Harvests subsets of related data, and other contextual information, cross-linking them to demonstrate Enhanced Publications
The OpenAIRE portal provides a suite of services
- deposit and access - guidelines and a helpdesk
OpenAIRE runs a series of workshops, and produces reports on Open Access issues
Why is OpenAIRE important? By facilitating Open Science and Open Access, OpenAIRE allows scientists to access, reuse and enhance and research output
OpenAIRE provides a cross-discipline support service for European Scientists
Tools such as publication usage statistics
OpenAIRE is based on
- versatile technology and innovative research - European outreach effort which advocates
Open Access
Who is OpenAIRE? OpenAIRE is an FP7 funded project, now in its second phase of funding until May 2014
41 project partners include 3 scientifi c communities: EBI, DANS and BADC
Collaboration with EuroCRIS, EUDAT, DataCite, COAR, LIBER, SPARC Europe
Project Coordinator: Mike Hatzopoulos, [email protected]
Services
Supports researchers
and third-parties to
search, access, and
reuse research
output
Infrastructure
OpenAIRE gathers
research output from repo-
sitory network, identifying
associated links and
enabling enhanced
publications
Research
Repositories
link to OpenAIRE:
publications, data,
funding
information
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Luxemburg (University of Luxemburg)Malta (Malta Council for Science & Technology and University of Malta)Netherlands (Utrecht University)Norway (University of Tromsoe)Poland (ICM ñ University of Warsaw)Portugal (University of Minho)Romania (Kosson)Slovakia (University Library of Bratislava)
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28.6.2012 36
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Coordination of Libraries?
A possible explanation is that to do something about the situation requires coordinated action. Even if one library refuses to subscribe to Elsevier journals, plenty of others will feel that they can’t refuse, and Elsevier won’t mind too much. But if all libraries were prepared to club together and negotiate jointly, doing a kind of reverse bundling — accept this deal or none of us will subscribe to any of your journals — then Elsevier’s profits (which are huge, by the way) would be genuinely threatened. However, it seems unlikely that any such massive coordination between libraries will ever take place.
Timothy Gowers, Mathematician, Cambridge University
„
http://gowers.wordpress.com/2012/01/21/elsevier-my-part-in-its-downfall/
37
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Van Noorden, Richard (2012) Nature 486, 302–303, http://dx.doi.org/10.1038/486302a
38
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PLOS: Public Library of Science
http://dx.doi.org/10.1371/journal.pone.0000308
39